Department of Statistics – Academic Commons Link to Recent Ph.D. Dissertations (2011 – present)
2022 Ph.D. Dissertations
Andrew Davison
Statistical Perspectives on Modern Network Embedding Methods
Sponsor: Tian Zheng
Nabarun Deb
Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing
Sponsor: Bodhisattva Sen / Co-Sponsor: Sumit Mukherjee
Elliot Gordon Rodriguez
Advances in Machine Learning for Compositional Data
Sponsor: John Cunningham
Charles Christopher Margossian
Modernizing Markov Chains Monte Carlo for Scientific and Bayesian Modeling
Sponsor: Andrew Gelman
Alejandra Quintos Lima
Dissertation TBA
Sponsor: Philip Protter
Bridgette Lynn Ratcliffe
Statistical approach to tagging stellar birth groups in the Milky Way
Sponsor: Bodhisattva Sen
Chengliang Tang
Latent Variable Models for Events on Social Networks
On Recovering the Best Rank-? Approximation from Few Entries
Sponsor: Ming Yuan
Sponsor: Sumit Mukherjee
2021 Ph.D. Dissertations
On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods
Sponsor: Liam Paninski
Advances in Statistical Machine Learning Methods for Neural Data Science
Milad Bakhshizadeh
Phase retrieval in the high-dimensional regime
Chi Wing Chu
Semiparametric Inference of Censored Data with Time-dependent Covariates
Miguel Angel Garrido Garcia
Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles
Sponsor: Ioannis Karatzas
Rishabh Dudeja
High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices
Sponsor: Arian Maleki
Statistical Learning for Process Data
Sponsor: Jingchen Liu
Toward a scalable Bayesian workflow
2020 Ph.D. Dissertations
Jonathan Auerbach
Some Statistical Models for Prediction
Sponsor: Shaw-Hwa Lo
Adji Bousso Dieng
Deep Probabilistic Graphical Modeling
Sponsor: David Blei
Guanhua Fang
Latent Variable Models in Measurement: Theory and Application
Sponsor: Zhiliang Ying
Promit Ghosal
Time Evolution of the Kardar-Parisi-Zhang Equation
Sponsor: Ivan Corwin
Partition-based Model Representation Learning
Sihan Huang
Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates
Peter JinHyung Lee
Spike Sorting for Large-scale Multi-electrode Array Recordings in Primate Retina
Statistical Analysis of Complex Data in Survival and Event History Analysis
Multiple Causal Inference with Bayesian Factor Models
New perspectives in cross-validation
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| DEPARTMENT OF STATISTICS Columbia University Room 1005 SSW, MC 4690 1255 Amsterdam Avenue New York, NY 10027 Phone: 212.851.2132 Fax: 212.851.2164 |
PhD Dissertations
| Title | Author | Supervisor |
|---|---|---|
| Estimation and Inference of Optimal Policies | , | |
| Statistical Learning and Modeling with Graphs and Networks | , |
| Title | Author | Supervisor |
|---|---|---|
| Statistical Methods for the Analysis and Prediction of Hierarchical Time Series Data with Applications to Demography | ||
| Exponential Family Models for Rich Preference Ranking Data | ||
| Bayesian methods for variable selection | , | |
| Statistical methods for genomic sequencing data | ||
| Inference and Estimation for Network Data | ||
| Mixture models to fit heavy-tailed, heterogeneous or sparse data | , | |
| Addressing double dipping through selective inference and data thinning | ||
| Methods for the Statistical Analysis of Preferences, with Applications to Social Science Data | ||
| Estimating subnational health and demographic indicators using complex survey data | ||
| Interpretation and Validation for unsupervised learning |
| Title | Author | Supervisor |
|---|---|---|
| Likelihood-based haplotype frequency modeling using variable-order Markov chains | ||
| Statistical Divergences for Learning and Inference: Limit Laws and Non-Asymptotic Bounds | , | |
| Causal Structure Learning in High Dimensions | , | |
| Missing Data Methods for Observational Health Dataset | ||
| Methods, Models, and Interpretations for Spatial-Temporal Public Health Applications | ||
| Statistical Methods for Clustering and High Dimensional Time Series Analysis | ||
| Geometric algorithms for interpretable manifold learning |
| Title | Author | Supervisor |
|---|---|---|
| Improving Uncertainty Quantification and Visualization for Spatiotemporal Earthquake Rate Models for the Pacific Northwest | , | |
| Statistical modeling of long memory and uncontrolled effects in neural recordings | ||
| Distribution-free consistent tests of independence via marginal and multivariate ranks | ||
| Causality, Fairness, and Information in Peer Review | , | |
| Subnational Estimation of Period Child Mortality in a Low and Middle Income Countries Context | ||
| Progress in nonparametric minimax estimation and high dimensional hypothesis testing | , | |
| Likelihood Analysis of Causal Models | ||
| Bayesian Models in Population Projections and Climate Change Forecast |
| Title | Author | Supervisor |
|---|---|---|
| Statistical Methods for Adaptive Immune Receptor Repertoire Analysis and Comparison | ||
| Statistical Methods for Geospatial Modeling with Stratified Cluster Survey Data | ||
| Representation Learning for Partitioning Problems | ||
| Estimation and Inference in Changepoint Models | ||
| Space-Time Contour Models for Sea Ice Forecasting | , | |
| Non-Gaussian Graphical Models: Estimation with Score Matching and Causal Discovery under Zero-Inflation | , | |
| Scalable Learning in Latent State Sequence Models |
| Title | Author | Supervisor |
|---|---|---|
| Latent Variable Models for Prediction & Inference with Proxy Network Measures | ||
| Bayesian Hierarchical Models and Moment Bounds for High-Dimensional Time Series | , | |
| Estimation and testing under shape constraints | , | |
| Inferring network structure from partially observed graphs | ||
| Fitting Stochastics Epidemic Models to Multiple Data Types | ||
| Realized genome sharing in random effects models for quantitative genetic traits | ||
| Large-Scale B Cell Receptor Sequence Analysis Using Phylogenetics and Machine Learning | ||
| Statistical Methods for Manifold Recovery and C^ (1, 1) Regression on Manifolds |
| Title | Author | Supervisor |
|---|---|---|
| Topics in Statistics and Convex Geometry: Rounding, Sampling, and Interpolation | ||
| Estimation and Testing Following Model Selection | ||
| Topics on Least Squares Estimation | ||
| Discovering Interaction in Multivariate Time Series | ||
| Nonparametric inference on monotone functions, with applications to observational studies | ||
| Bayesian Methods for Graphical Models with Limited Data | ||
| Model-Based Penalized Regression | ||
| Parameter Identification and Assessment of Independence in Multivariate Statistical Modeling | ||
| Preferential sampling and model checking in phylodynamic inference | ||
| Linear Structural Equation Models with Non-Gaussian Errors: Estimation and Discovery | ||
| Coevolution Regression and Composite Likelihood Estimation for Social Networks |
| Title | Author | Supervisor |
|---|---|---|
| "Scalable Methods for the Inference of Identity by Descent" | ||
| "Applications of Robust Statistical Methods in Quantitative Finance" | ||
| "Scalable Manifold Learning and Related Topics" | ||
| "Topics in Graph Clustering" | ||
| "Methods for Estimation and Inference for High-Dimensional Models" | , |
| Title | Author | Supervisor |
|---|---|---|
| "Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling" | ||
| "Space-Time Smoothing Models for Surveillance and Complex Survey Data" | ||
| "Testing Independence in High Dimensions & Identifiability of Graphical Models" | ||
| "Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes" | ||
| "Bayesian Methods for Inferring Gene Regulatory Networks" | , | |
| "Finite Sampling Exponential Bounds" | ||
| "Finite Population Inference for Causal Parameters" | ||
| "Projection and Estimation of International Migration" |
| Title | Author | Supervisor |
|---|---|---|
| "Theory and Methods for Tensor Data" | ||
| "Discrete-Time Threshold Regression for Survival Data with Time-Dependent Covariates" | ||
| "Degeneracy, Duration, and Co-Evolution: Extending Exponential Random Graph Models (ERGM) for Social Network Analysis" | ||
| "The Likelihood Pivot: Performing Inference with Confidence" | ||
| "Lord's Paradox and Targeted Interventions: The Case of Special Education" | , | |
| "Bayesian Modeling of a High Resolution Housing Price Index" | ||
| "Phylogenetic Stochastic Mapping" |
| Title | Author | Supervisor |
|---|---|---|
| "R-Squared Inference Under Non-Normal Error" | ||
| "Monte Carlo Estimation of Identity by Descent in Populations" | ||
| "Bayesian Spatial and Temporal Methods for Public Health Data" | , | |
| "Functional Quantitative Genetics and the Missing Heritability Problem" | ||
| "Predictive Modeling of Cholera Outbreaks in Bangladesh" | , | |
| "Gravimetric Anomaly Detection Using Compressed Sensing" |
| Title | Author | Supervisor |
|---|---|---|
| "Statistical Inference Using Kronecker Structured Covariance" | ||
| "Learning and Manifolds: Leveraging the Intrinsic Geometry" | ||
| "An Algorithmic Framework for High Dimensional Regression with Dependent Variables" | ||
| "Bayesian Population Reconstruction: A Method for Estimating Age- and Sex-Specific Vital Rates and Population Counts with Uncertainty from Fragmentary Data" | ||
| "Bayesian Nonparametric Inference of Effective Population Size Trajectories from Genomic Data" | ||
| "Modeling Heterogeneity Within and Between Matrices and Arrays" | ||
| "Shape-Constrained Inference for Concave-Transformed Densities and their Modes" |
| Title | Author | Supervisor |
|---|---|---|
| "Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data" | ||
| "Tests for Differences between Least Squares and Robust Regression Parameter Estimates and Related To Pics" | ||
| "Bayesian Modeling of Health Data in Space and Time" | ||
| "Coordinate-Free Exponential Families on Contingency Tables" | , |
| Title | Author | Supervisor |
|---|---|---|
| "Statistical Approaches to Analyze Mass Spectrometry Data Graduating Year" | , | |
| "Seeing the trees through the forest; a competition model for growth and mortality" | ||
| "Bayesian Inference of Exponential-family Random Graph Models for Social Networks" | ||
| "Statistical Models for Estimating and Predicting HIV/AIDS Epidemics" | ||
| "Modeling the Game of Soccer Using Potential Functions" | ||
| "Parametrizations of Discrete Graphical Models" | ||
| "A Bayesian Surveillance System for Detecting Clusters of Non-Infectious Diseases" |
| Title | Author | Supervisor |
|---|---|---|
| "Estimating social contact networks to improve epidemic simulation models" | ||
| "Convex analysis methods in shape constrained estimation." | ||
| "Multivariate Geostatistics and Geostatistical Model Averaging" | ||
| "Covariance estimation in the Presence of Diverse Types of Data" | ||
| "Portfolio Optimization with Tail Risk Measures and Non-Normal Returns" |
| Title | Author | Supervisor |
|---|---|---|
| "Statistical Models for Social Network Data and Processes" | ||
| "Models for Heterogeneity in Heterosexual Partnership Networks" | ||
| "A comparison of alternative methodologies for estimation of HIV incidence" | ||
| "Bayesian Model Averaging and Multivariate Conditional Independence Structures" | ||
| "Conditional tests for localizing trait genes" | ||
| "Combining and Evaluating Probabilistic Forecasts" | ||
| "Probabilistic weather forecasting using Bayesian model averaging" | ||
| "Statistical Analysis of Portfolio Risk and Performance Measures: the Influence Function Approach" | ||
| "Factor Model Monte Carlo Methods for General Fund-of-Funds Portfolio Management" |
| Title | Author | Supervisor |
|---|---|---|
| "Statistical methods for peptide and protein identification using mass spectrometry" | ||
| "Inference from partially-observed network data" | ||
| "Models and Inference of Transmission of DNA Methylation Patterns in Mammalian Somatic Cells" | ||
| "Estimates and projections of the total fertility rate" | ||
| "Nonparametric estimation of multivariate monotone densities" | ||
| "Learning transcriptional regulatory networks from the integration of heterogeneous high-throughout data" | ||
| "Extensions of Latent Class Transition Models with Application to Chronic Disability Survey Data" | ||
| "Statistical Solutions to Some Problems in Medical Imaging" | , |
| Title | Author | Supervisor |
|---|---|---|
| ""Up-and-Down" and the Percentile-Finding Problem" | ||
| "Statistical Methodology for Longitudinal Social Network Data" | ||
| "Probabilistic weather forecasting with spatial dependence" | ||
| "Wavelet variance analysis for time series and random fields" | , | |
| "Bayesian hierarchical curve registration" |
| Title | Author | Supervisor |
|---|---|---|
| "Goodness-of-fit statistics based on phi-divergences" | ||
| "An efficient and flexible model for patterns of population genetic variation" | ||
| "Learning in Spectral Clustering" | ||
| "Variable selection and other extensions of the mixture model clustering framework" | ||
| "Algorithms for Estimating the Cluster Tree of a Density" | ||
| "Likelihood inference for population structure, using the coalescent" | ||
| "Exploring rates and patterns of variability in gene conversion and crossover in the human genome" | ||
| "Alleviating ecological bias in generalized linear models and optimal design with subsample data" | , | |
| "Nonparametric estimation for current status data with competing risks" | , |
| Title | Author | Supervisor |
|---|---|---|
| "Bayesian robust analysis of gene expression microarray data" | ||
| "Alternative models for estimating genetic maps from pedigree data" | ||
| "Allele-sharing methods for linkage detection using extended pedigrees" | ||
| "Robust estimation of factor models in finance" | ||
| "Using the structure of d-connecting paths as a qualitative measure of the strength of dependence" | , | |
| "Alternative estimators of wavelet variance" | , , |
| Title | Author | Supervisor |
|---|---|---|
| "Maximum likelihood estimation in Gaussian AMP chain graph models and Gaussian ancestral graph models" | , | |
| "Nonparametric estimation of a k-monotone density: A new asymptotic distribution theory" |
| Title | Author | Supervisor |
|---|---|---|
| "The genetic structure of related recombinant lines" | ||
| "Joint relationship inference from three or more individuals in the presence of genotyping error" | ||
| "Personal characteristics and covariate measurement error in disease risk estimation" | , | |
| "Model based and hybrid clustering of large datasets" | , |
| Title | Author | Supervisor |
|---|---|---|
| "Generalized linear mixed models: development and comparison of different estimation methods" | ||
| "Practical importance sampling methods for finite mixture models and multiple imputation" | ||
| "Applying graphical models to partially observed data-generating processes" | , |
| Title | Author | Supervisor |
|---|---|---|
| "Bayesian inference for deterministic simulation models for environmental assessment" | ||
| "Modeling recessive lethals: An explanation for excess sharing in siblings" | ||
| "Estimation with bivariate interval censored data" | ||
| "Latent models for cross-covariance" | , |
| Title | Author | Supervisor |
|---|---|---|
| "A model selection approach to partially linear regression" | ||
| "Wavelet-based estimation for trend contaminated long memory processes" | , | |
| "Global covariance modeling: A deformation approach to anisotropy" | ||
| "Likelihood inference for parameteric models of dispersal" | ||
| "Bayesian inference in hidden stochastic population processes" | ||
| "Logic regression and statistical issues related to the protein folding problem" | , | |
| "Likelihood ratio inference in regular and non-regular problems" | ||
| "Estimating the association between airborne particulate matter and elderly mortality in Seattle, Washington using Bayesian Model Averaging" | , | |
| "Nonhomogeneous hidden Markov models for downscaling synoptic atmospheric patterns to precipitation amounts" | , | |
| "Detecting and extracting complex patterns from images and realizations of spatial point processes" |
| Title | Author | Supervisor |
|---|---|---|
| "Statistical approaches to distinct value estimation" | , | |
| "Generalization of boosting algorithms and applications of Bayesian inference for massive datasets" | , | |
| "Bayesian inference for noninvertible deterministic simulation models, with application to bowhead whale assessment" | ||
| "Monte Carlo likelihood calculation for identity by descent data" | ||
| "Fast automatic unsupervised image segmentation and curve detection in spatial point processes" | ||
| "Semiparametric inference based on estimating equations in regressions models for two phase outcome dependent sampling" | , | |
| "Capture-recapture estimation of bowhead whale population size using photo-identification data" | , | |
| "Lifetime and disease onset distributions from incomplete observations" |
| Title | Author | Supervisor |
|---|---|---|
| "Additive mixture models for multichannel image data" | ||
| "Application of ridge regression for improved estimation of parameters in compartmental models" | ||
| "Bayesian modeling of highly structured systems using Markov chain Monte Carlo" | ||
| "Assessing nonstationary time series using wavelets" | , | |
| "Lattice conditional independence models for incomplete multivariate data and for seemingly unrelated regressions" | , | |
| "Estimation for counting processes with incomplete data" | ||
| "Regularization techniques for linear regression with a large set of carriers" | ||
| "Large sample theory for pseudo maximum likelihood estimates in semiparametric models" |
| Title | Author | Supervisor |
|---|---|---|
| "A new learning procedure in acyclic directed graphs" | ||
| "Phylogenies via conditional independence modeling" | ||
| "Bayesian model averaging in censored survival models" | ||
| "Bayesian information retrieval" | ||
| "Statistical inference for partially observed markov population processes" | ||
| "Tools for the advancement of undergraduate statistics education" |
| Title | Author | Supervisor |
|---|---|---|
| "Bootstrapping functional m-estimators" | ||
| "Variability estimation in linear inverse problems" | ||
| "Inference in a discrete parameter space" |
| Title | Author | Supervisor |
|---|---|---|
| "Estimation of heterogeneous space-time covariance" | ||
| "Semiparametric estimation of major gene and random environmental effects for age of onset" | ||
| "Statistical analysis of biological monitoring data: State-space models for species compositions" |
| Title | Author | Supervisor |
|---|---|---|
| "Estimation in regression models with interval censoring" | ||
| "Spatial applications of Markov chain Monte Carlo for bayesian inference" | ||
| "Accounting for model uncertainty in linear regression" | ||
| "Robust estimation in point processes" | ||
| "Multilevel modeling of discrete event history data using Markov chain Monte Carlo methods" |
| Title | Author | Supervisor |
|---|---|---|
| "A Bayesian framework and importance sampling methods for synthesizing multiple sources of evidence and uncertainty linked by a complex mechanistic model" | ||
| "State-space modeling of salmon migration and Monte Carlo Alternatives to the Kalman filter" | ||
| "The Poisson clumping heuristic and the survival of genome in small pedigrees" | ||
| "Markov chain Monte Carlo estimates of probabilities on complex structures" | ||
| "A class of stochastic models for relating synoptic atmospheric patterns to local hydrologic phenomena" |
| Title | Author | Supervisor |
|---|---|---|
| "Bayesian methods for the analysis of misclassified or incomplete multivariate discrete data" | ||
| "Auxiliary and missing covariate problems in failure time regression analysis" | ||
| "A high order hidden markov model" |
| Title | Author | Supervisor |
|---|---|---|
| "General-weights bootstrap of the empirical process" | ||
| "The weighted likelihood bootstrap and an algorithm for prepivoting" |
| Title | Author | Supervisor |
|---|---|---|
| "Modelling agricultural field trials in the presence of outliers and fertility jumps" | ||
| "Modeling and bootstrapping for non-gaussian time series" | ||
| "Genetic restoration on complex pedigrees" | ||
| "Incorporating covariates into a beta-binomial model with applications to medicare policy: A Bayes/empirical Bayes approach" | ||
| "Likelihood and exponential families" |
| Title | Author | Supervisor |
|---|---|---|
| "Estimation of mixing and mixed distributions" | ||
| "Classical inference in spatial statistics" |
| Title | Author | Supervisor |
|---|---|---|
| "Exploratory methods for censored data" | ||
| "Aspects of robust analysis in designed experiments" | ||
| "Diagnostics for time series models" | ||
| "Constrained cluster analysis and image understanding" |
| Title | Author | Supervisor |
|---|---|---|
| "Kullback-Leibler estimation of probability measures with an application to clustering" | ||
| "Time series models for continuous proportions" | ||
| "The data viewer: A program for graphical data analysis" | ||
| "Additive principal components: A method for estimating additive constraints with small variance from multivariate data" |
| Title | Author | Supervisor |
|---|---|---|
| "Estimation for infinite variance autoregressive processes" | ||
| "A computer system for Monte Carlo experimentation" |
| Title | Author | Supervisor |
|---|---|---|
| "Robust estimation for the errors-in-variables model" | ||
| "Robust statistics on compact metric spaces" | ||
| "Weak convergence and a law of the iterated logarithm for processes indexed by points in a metric space" |
| Title | Author | Supervisor |
|---|---|---|
| "The statistics of long memory processes" |
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Digital Commons @ USF > College of Arts and Sciences > Mathematics and Statistics > Theses and Dissertations
Mathematics and Statistics Theses and Dissertations
Theses/dissertations from 2024 2024.
The Effect of Fixed Time Delays on the Synchronization Phase Transition , Shaizat Bakhytzhan
On the Subelliptic and Subparabolic Infinity Laplacian in Grushin-Type Spaces , Zachary Forrest
Utilizing Machine Learning Techniques for Accurate Diagnosis of Breast Cancer and Comprehensive Statistical Analysis of Clinical Data , Myat Ei Ei Phyo
Quandle Rings, Idempotents and Cocycle Invariants of Knots , Dipali Swain
Comparative Analysis of Time Series Models on U.S. Stock and Exchange Rates: Bayesian Estimation of Time Series Error Term Model Versus Machine Learning Approaches , Young Keun Yang
Theses/Dissertations from 2023 2023
Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani
Applied Analysis for Learning Architectures , Himanshu Singh
Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze
Theses/Dissertations from 2022 2022
New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana
Advances and Applications of Optimal Polynomial Approximants , Raymond Centner
Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty
On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly
Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He
Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias
Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi
A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman
Theses/Dissertations from 2021 2021
Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri
Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi
Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou
Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando
Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu
Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang
Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang
Theses/Dissertations from 2020 2020
Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi
Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun
Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu
On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman
Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek
Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen
Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan
Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop
On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink
Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang
Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala
Theses/Dissertations from 2019 2019
Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian
Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar
Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil
Fractional Random Weighted Bootstrapping for Classification on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter
Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz
Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi
Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi
Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos
The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva
Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak
Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich
An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado
Power Graphs of Quasigroups , DayVon L. Walker
Theses/Dissertations from 2018 2018
Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed
Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai
A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah
Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa
Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields
Generalizations of Quandles and their cohomologies , Matthew J. Green
Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu
Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou
Human Activity Recognition Based on Transfer Learning , Jinyong Pang
Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham
Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel
Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova
Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang
Theses/Dissertations from 2017 2017
Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack
Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon
On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill
Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill
Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly
Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera
Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao
Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati
Dynamics of Multicultural Social Networks , Kristina B. Hilton
Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi
Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally
Patterns in Words Related to DNA Rearrangements , Lukas Nabergall
Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na
Schreier Graphs of Thompson's Group T , Allen Pennington
Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya
Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo
Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi
Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou
Theses/Dissertations from 2016 2016
A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea
Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery
Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman
On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr
Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim
Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano
Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure
Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru
Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park
Leonard Systems and their Friends , Jonathan Spiewak
Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun
Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu
Global Attractors and Random Attractors of Reaction-Diffusion Systems , Junyi Tu
Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classification and Clustering , Xing Wang
On Spectral Properties of Single Layer Potentials , Seyed Zoalroshd
Theses/Dissertations from 2015 2015
Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach , Wei Chen
Active Tile Self-assembly and Simulations of Computational Systems , Daria Karpenko
Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance , Vindya Kumari Pathirana
Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data , Taysseer Sharaf
Radial Versus Othogonal and Minimal Projections onto Hyperplanes in l_4^3 , Richard Alan Warner
Ensemble Learning Method on Machine Maintenance Data , Xiaochuang Zhao
Theses/Dissertations from 2014 2014
Properties of Graphs Used to Model DNA Recombination , Ryan Arredondo
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Home > Statistics > Dissertations, Theses, and Student Work
Statistics, Department of
Department of statistics: dissertations, theses, and student work.
Measuring Jury Perception of Explainable Machine Learning and Demonstrative Evidence , Rachel Rogers
Examining the Effect of Word Embeddings and Preprocessing Methods on Fake News Detection , Jessica Hauschild
Exploring Experimental Design and Multivariate Analysis Techniques for Evaluating Community Structure of Bacteria in Microbiome Data , Kelsey Karnik
Human Perception of Exponentially Increasing Data Displayed on a Log Scale Evaluated Through Experimental Graphics Tasks , Emily Robinson
Factors Influencing Student Outcomes in a Large, Online Simulation-Based Introductory Statistics Course , Ella M. Burnham
Comparing Machine Learning Techniques with State-of-the-Art Parametric Prediction Models for Predicting Soybean Traits , Susweta Ray
Using Stability to Select a Shrinkage Method , Dean Dustin
Statistical Methodology to Establish a Benchmark for Evaluating Antimicrobial Resistance Genes through Real Time PCR assay , Enakshy Dutta
Group Testing Identification: Objective Functions, Implementation, and Multiplex Assays , Brianna D. Hitt
Community Impact on the Home Advantage within NCAA Men's Basketball , Erin O'Donnell
Optimal Design for a Causal Structure , Zaher Kmail
Role of Misclassification Estimates in Estimating Disease Prevalence and a Non-Linear Approach to Study Synchrony Using Heart Rate Variability in Chickens , Dola Pathak
A Characterization of a Value Added Model and a New Multi-Stage Model For Estimating Teacher Effects Within Small School Systems , Julie M. Garai
Methods to Account for Breed Composition in a Bayesian GWAS Method which Utilizes Haplotype Clusters , Danielle F. Wilson-Wells
Beta-Binomial Kriging: A New Approach to Modeling Spatially Correlated Proportions , Aimee Schwab
Simulations of a New Response-Adaptive Biased Coin Design , Aleksandra Stein
MODELING THE DYNAMIC PROCESSES OF CHALLENGE AND RECOVERY (STRESS AND STRAIN) OVER TIME , Fan Yang
A New Approach to Modeling Multivariate Time Series on Multiple Temporal Scales , Tucker Zeleny
A Reduced Bias Method of Estimating Variance Components in Generalized Linear Mixed Models , Elizabeth A. Claassen
NEW STATISTICAL METHODS FOR ANALYSIS OF HISTORICAL DATA FROM WILDLIFE POPULATIONS , Trevor Hefley
Informative Retesting for Hierarchical Group Testing , Michael S. Black
A Test for Detecting Changes in Closed Networks Based on the Number of Communications Between Nodes , Christopher S. Wichman
GROUP TESTING REGRESSION MODELS , Boan Zhang
A Comparison of Spatial Prediction Techniques Using Both Hard and Soft Data , Megan L. Liedtke Tesar
STUDYING THE HANDLING OF HEAT STRESSED CATTLE USING THE ADDITIVE BI-LOGISTIC MODEL TO FIT BODY TEMPERATURE , Fan Yang
Estimating Teacher Effects Using Value-Added Models , Jennifer L. Green
SEQUENCE COMPARISON AND STOCHASTIC MODEL BASED ON MULTI-ORDER MARKOV MODELS , Xiang Fang
DETECTING DIFFERENTIALLY EXPRESSED GENES WHILE CONTROLLING THE FALSE DISCOVERY RATE FOR MICROARRAY DATA , SHUO JIAO
Spatial Clustering Using the Likelihood Function , April Kerby
FULLY EXPONENTIAL LAPLACE APPROXIMATION EM ALGORITHM FOR NONLINEAR MIXED EFFECTS MODELS , Meijian Zhou
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Doctoral Program
Program summary.
Students are required to
- master the material in the prerequisite courses
- pass the first-year core program
- attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year)
- confirm a principal dissertation research advisor and file for candidacy (early spring quarter of 2nd year)
- satisfy the depth and breadth requirements (2nd/3rd/4th year)
- successfully complete the thesis proposal meeting and submit the Dissertation Reading Committee form (winter quarter of the 3rd year)
- present a draft of their dissertation and pass the university oral examination (4th/5th year)
The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.
All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in the Department of Statistics PhD Student Handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).
Statistics Department PhD Handbook
All students are expected to abide by the Honor Code and the Fundamental Standard .
Doctoral and Research Advisors
During the first two years of the program, students' academic progress is monitored by the department's Director of Graduate Studies (DGS). Each student should meet at least once a quarter with the DGS to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.
Qualifying Examinations and Candidacy
Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for PhD Candidacy, a university milestone, by early spring quarter of their second year.
While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.
Thesis Proposal Meeting and Dissertation Reading Committee
The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.
The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member.
The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.
For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.
University Oral Examinations
The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.
The Dissertation Reading Committee must also read and approve the thesis.
For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .
Dissertation
The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's Dissertation Reading Committee. Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members.
PhD in Statistics
The PhD degree in statistics is designed for students who wish to pursue a career in statistics research in academia, government, or industry. The curriculum is designed to provide a strong in-depth and broad training in statistical theory, methodology, computation, and applications. Students begin their research experience in the first year and participate in on- or off-campus internships in the second year. These provide a well-rounded, solid education for graduates to assume and advance their roles as university professors, senior statisticians, or data scientists.
Dissertations
While PhD students are engaged in research from the first year, they formally begin their dissertation work after completing their doctoral preliminary exams. Dissertations may be oriented toward applied statistics, computational methods, theoretical statistics, or probability. It typically takes one to two years to complete and defend the dissertation work. The dissertation is expected to be of publishable quality in reputable academic journals. Almost all PhD students complete the degree in five years.
College Resources for Graduate Students
Visit CLA’s website for graduate students to learn about collegiate funding opportunities, student support, career services, and more.
Student Services Career Services Funding & Support
Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.
Choosing a Field of Study
There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.
There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .
Residency Requirements
Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.
Your Advisor and Special Committee
The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.
The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.
Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.
The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage .
Statistics PhD Travel Support
The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information.
Completion of the PhD Degree
In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.
Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.
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PhD Program information
The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests. Many students are involved in interdisciplinary research. Students may also have the option to pursue a designated emphasis (DE) which is an interdisciplinary specialization: Designated Emphasis in Computational and Genomic Biology , Designated Emphasis in Computational Precision Health , Designated Emphasis in Computational and Data Science and Engineering . The program requires four semesters of residence.
Normal progress entails:
Year 1 . Perform satisfactorily in preliminary coursework. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity. Years 2-3 . Continue coursework. Find a thesis advisor and an area for the oral qualifying exam. Formally choose a chair for qualifying exam committee, who will also serve as faculty mentor separate from the thesis advisor. Pass the oral qualifying exam and advance to candidacy by the end of Year 3. Present research at BSTARS each year. Years 4-5 . Finish the thesis and give a lecture based on it in a department seminar.
Program Requirements
- Qualifying Exam
Course work and evaluation
Preliminary stage: the first year.
Effective Fall 2019, students are expected to take four semester-long courses for a letter grade during their first year which should be selected from the core first-year PhD courses offered in the department: Probability (204/205A, 205B,), Theoretical Statistics (210A, 210B), and Applied Statistics (215A, 215B). These requirements can be altered by a member of the PhD Program Committee (in consultation with the faculty mentor and by submitting a graduate student petition ) in the following cases:
- Students primarily focused on probability will be allowed to substitute one semester of the four required semester-long courses with an appropriate course from outside the department.
- Students may request to postpone one semester of the core PhD courses and complete it in the second year, in which case they must take a relevant graduate course in their first year in its place. In all cases, students must complete the first year requirements in their second year as well as maintain the overall expectations of second year coursework, described below. Some examples in which such a request might be approved are described in the course guidance below.
- Students arriving with advanced standing, having completed equivalent coursework at another institution prior to joining the program, may be allowed to take other relevant graduate courses at UC Berkeley to satisfy some or all of the first year requirements
Requirements on course work beyond the first year
Students entering the program before 2022 are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.
Starting with the cohort entering in the 2022-23 academic year , students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge.
For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U). Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the course should be numbered 204-272 to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.
First year course work: For the purposes of satisfactory progression in the first year, grades in the core PhD courses are evaluated as: A+: Excellent performance in PhD program A: Good performance in PhD program A-: Satisfactory performance B+: Performance marginal, needs improvement B: Unsatisfactory performance First year and beyond: At the end of each year, students must meet with his or her faculty mentor to review their progress and assess whether the student is meeting expected milestones. The result of this meeting should be the completion of the student’s annual review form, signed by the mentor ( available here ). If the student has a thesis advisor, the thesis advisor must also sign the annual review form.
Guidance on choosing course work
Choice of courses in the first year: Students enrolling in the fall of 2019 or later are required to take four semesters of the core PhD courses, at least three of which must be taken in their first year. Students have two options for how to schedule their four core courses:
- Option 1 -- Complete Four Core Courses in 1st year: In this option, students would take four core courses in the first year, usually finishing the complete sequence of two of the three sequences. Students following this option who are primarily interested in statistics would normally take the 210A,B sequence (Theoretical Statistics) and then one of the 205A,B sequence (Probability) or the 215A,B sequence (Applied Statistics), based on their interests, though students are allowed to mix and match, where feasible. Students who opt for taking the full 210AB sequence in the first year should be aware that 210B requires some graduate-level probability concepts that are normally introduced in 205A (or 204).
- Option 2 -- Postponement of one semester of a core course to the second year: In this option, students would take three of the core courses in the first year plus another graduate course, and take the remaining core course in their second year. An example would be a student who wanted to take courses in each of the three sequences. Such a student could take the full year of one sequence and the first semester of another sequence in the first year, and the first semester of the last sequence in the second year (e.g. 210A, 215AB in the first year, and then 204 or 205A in the second year). This would also be a good option for students who would prefer to take 210A and 215A in their first semester but are concerned about their preparation for 210B in the spring semester. Similarly, a student with strong interests in another discipline, might postpone one of the spring core PhD courses to the second year in order to take a course in that discipline in the first year. Students who are less mathematically prepared might also be allowed to take the upper division (under-graduate) courses Math 104 and/or 105 in their first year in preparation for 205A and/or 210B in their second year. Students who wish to take this option should consult with their faculty mentor, and then must submit a graduate student petition to the PhD Committee to request permission for postponement. Such postponement requests will be generally approved for only one course. At all times, students must take four approved graduate courses for a letter grade in their first year.
After the first year: Students with interests primarily in statistics are expected to take at least one semester of each of the core PhD sequences during their studies. Therefore at least one semester (if not both semesters) of the remaining core sequence would normally be completed during the second year. The remaining curriculum for the second and third years would be filled out with further graduate courses in Statistics and with courses from other departments. Students are expected to acquire some experience and proficiency in computing. Students are also expected to attend at least one departmental seminar per week. The precise program of study will be decided in consultation with the student’s faculty mentor.
Remark. Stat 204 is a graduate level probability course that is an alternative to 205AB series that covers probability concepts most commonly found in the applications of probability. It is not taught all years, but does fulfill the requirements of the first year core PhD courses. Students taking Stat 204, who wish to continue in Stat 205B, can do so (after obtaining the approval of the 205B instructor), by taking an intensive one month reading course over winter break.
Designated Emphasis: Students with a Designated Emphasis in Computational and Genomic Biology or Designated Emphasis in Computational and Data Science and Engineering should, like other statistics students, acquire a firm foundation in statistics and probability, with a program of study similar to those above. These programs have additional requirements as well. Interested students should consult with the graduate advisor of these programs.
Starting in the Fall of 2019, PhD students are required in their first year to take four semesters of the core PhD courses. Students intending to specialize in Probability, however, have the option to substitute an advanced mathematics class for one of these four courses. Such students will thus be required to take Stat 205A/B in the first year, at least one of Stat 210A/B or Stat 215A/B in the first year, in addition to an advanced mathematics course. This substitute course will be selected in consultation with their faculty mentor, with some possible courses suggested below. Students arriving with advanced coursework equivalent to that of 205AB can obtain permission to substitute in other advanced probability and mathematics coursework during their first year, and should consult with the PhD committee for such a waiver.
During their second and third years, students with a probability focus are expected to take advanced probability courses (e.g., Stat 206 and Stat 260) to fulfill the coursework requirements that follow the first year. Students are also expected to attend at least one departmental seminar per week, usually the probability seminar. If they are not sufficiently familiar with measure theory and functional analysis, then they should take one or both of Math 202A and Math 202B. Other recommended courses from the department of Mathematics or EECS include:
Math 204, 222 (ODE, PDE) Math 205 (Complex Analysis) Math 258 (Classical harmonic analysis) EE 229 (Information Theory and Coding) CS 271 (Randomness and computation)
The Qualifying Examination
The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department. At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.
Qualifying Exam Chair. For qualifying exam committees formed in the Fall of 2019 or later, the qualifying exam chair will also serve as the student’s departmental mentor, unless a student already has two thesis advisors. The student must select a qualifying exam chair and obtain their agreement to serve as their qualifying exam chair and faculty mentor. The student's prospective thesis advisor cannot chair the examination committee. Selection of the chair can be done well in advance of the qualifying exam and the rest of the qualifying committee, and because the qualifying exam chair also serves as the student’s departmental mentor (unless the student has co-advisors), the chair is expected to be selected by the beginning of the third year or at the beginning of the semester of the qualifying exam, whichever comes earlier. For more details regarding the selection of the Qualifying Exam Chair, see the "Mentoring" tab.
Paperwork and Application. Students at the point of taking a qualifying exam are assumed to have already found a thesis advisor and to should have already submitted the internal departmental form to the Graduate Student Services Advisor ( found here ). Selection of a qualifying exam chair requires that the faculty member formally agree by signing the internal department form ( found here ) and the student must submit this form to the Graduate Student Services Advisor. In order to apply to take the exam, the student must submit the Application for the Qualifying Exam via CalCentral at least three weeks prior to the exam. If the student passes the exam, they can then officially advance to candidacy for the Ph.D. If the student fails the exam, the committee may vote to allow a second attempt. Regulations of the Graduate Division permit at most two attempts to pass the oral qualifying exam. After passing the exam, the student must submit the Application for Candidacy via CalCentral .
The Doctoral Thesis
The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division. See Alumni if you would like to view thesis titles of former PhD Students.
Graduate Division offers various resources, including a workshop, on how to write a thesis, from beginning to end. Requirements for the format of the thesis are rather strict. For workshop dates and guidelines for submitting a dissertation, visit the Graduate Division website.
Students who have advanced from candidacy (i.e. have taken their qualifying exam and submitted the advancement to candidacy application) must have a joint meeting with their QE chair and their PhD advisor to discuss their thesis progression; if students are co-advised, this should be a joint meeting with their co-advisors. This annual review is required by Graduate Division. For more information regarding this requirement, please see https://grad.berkeley.edu/ policy/degrees-policy/#f35- annual-review-of-doctoral- candidates .
Teaching Requirement
For students enrolled in the graduate program before Fall 2016, students are required to serve as a Graduate Student Instructor (GSI) for a minimum of 20 hours (equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.
Effective with the Fall 2016 entering class, students are required to serve as a GSI for a minimum of two 50% GSI appointment during the regular academic semesters prior to graduation (20 hours a week is equivalent to a 50% GSI appointment for a semester) for Statistics courses numbered 150 and above. Exceptions to this policy are routinely made by the department.
Each spring, the department hosts an annual conference called BSTARS . Both students and industry alliance partners present research in the form of posters and lightning talks. All students in their second year and beyond are required to present a poster at BSTARS each year. This requirement is intended to acclimate students to presenting their research and allow the department generally to see the fruits of their research. It is also an opportunity for less advanced students to see examples of research of more senior students. However, any students who do not yet have research to present can be exempted at the request of their thesis advisor (or their faculty mentors if an advisor has not yet been determined).
Mentoring for PhD Students
Initial Mentoring: PhD students will be assigned a faculty mentor in the summer before their first year. This faculty mentor at this stage is not expected to be the student’s PhD advisor nor even have research interests that closely align with the student. The job of this faculty mentor is primarily to advise the student on how to find a thesis advisor and in selecting appropriate courses, as well as other degree-related topics such as applying for fellowships. Students should meet with their faculty mentors twice a semester. This faculty member will be the designated faculty mentor for the student during roughly their first two years, at which point students will find a qualifying exam chair who will take over the role of mentoring the student.
Research-focused mentoring : Once students have found a thesis advisor, that person will naturally be the faculty member most directly overseeing the student’s progression. However, students will also choose an additional faculty member to serve as a the chair of their qualifying exam and who will also serve as a faculty mentor for the student and as a member of his/her thesis committee. (For students who have two thesis advisors, however, there is not an additional faculty mentor, and the quals chair does NOT serve as the faculty mentor).
The student will be responsible for identifying and asking a faculty member to be the chair of his/her quals committee. Students should determine their qualifying exam chair either at the beginning of the semester of the qualifying exam or in the fall semester of the third year, whichever is earlier. Students are expected to have narrowed in on a thesis advisor and research topic by the fall semester of their third year (and may have already taken qualifying exams), but in the case where this has not happened, such students should find a quals chair as soon as feasible afterward to serve as faculty mentor.
Students are required to meet with their QE chair once a semester during the academic year. In the fall, this meeting will generally be just a meeting with the student and the QE chair, but in the spring it must be a joint meeting with the student, the QE chair, and the PhD advisor. If students are co-advised, this should be a joint meeting with their co-advisors.
If there is a need for a substitute faculty mentor (e.g. existing faculty mentor is on sabbatical or there has been a significant shift in research direction), the student should bring this to the attention of the PhD Committee for assistance.
PhD Student Forms:
Important milestones: .
Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO. If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding (including GSI or GSR appointments, and many fellowships).
| Identify PhD Advisor† | End of 2nd year |
| Identify Research Mentor (QE Chair) | Fall semester of 3rd year |
| Pass Qualifying Exam and Advance to Candidacy | End of 3rd year |
| Thesis Submission | End of 4th or 5th year |
†Students who are considering a co-advisor, should have at least one advisor formally identified by the end of the second year; the co-advisor should be identified by the end of the fall semester of the 3rd year in lieu of finding a Research Mentor/QE Chair.
Expected Progress Reviews:
| Spring 1st year | Annual Progress Review | Faculty Mentor |
| Review of 1st year progress | Head Graduate Advisor | |
| Spring 2nd year | Annual Progress Review | Faculty Mentor or Thesis Advisor(s) (if identified) |
| Fall 3+ year | Research progress report* | Research mentor** |
| Spring 3+ year | Annual Progress Review* | Jointly with PhD advisor(s) and Research mentor |
* These meetings do not need to be held in the semester that you take your Qualifying Exam, since the relevant people should be members of your exam committee and will discuss your research progress during your qualifying exam
** If you are being co-advised by someone who is not your primary advisor because your primary advisor cannot be your sole advisor, you should be meeting with that person like a research mentor, if not more frequently, to keep them apprised of your progress. However, if both of your co-advisors are leading your research (perhaps independently) and meeting with you frequently throughout the semester, you do not need to give a fall research progress report.
PhD in Statistics
Program description.
The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry. The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas. To enter, students need a bachelor’s degree in mathematics, statistics, or a closely related discipline. Students graduating with a PhD in Statistics are expected to:
- Demonstrate an understanding the core principles of Probability Theory, Estimation Theory, and Statistical Methods.
- Demonstrate the ability to conduct original research in statistics.
- Demonstrate the ability to present research-level statistics in a formal lecture
Requirements for the Ph.D. (Statistics Track)
Course Work A Ph.D. student in our department must complete sixteen courses for the Ph.D. At most, four of these courses may be transferred from another institution. If the Ph.D. student is admitted to the program at the post-Master’s level, then eight courses are usually required.
Qualifying Examinations First, all Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). Then, to qualify a student to begin work on a PhD dissertation, they must pass two of the following three exams at the PhD level: probability, mathematical statistics, and applied statistics. The probability and mathematical statistics exams are offered every September and the applied statistics exam is offered every April.
- PhD Exam in Probability: This exam covers the material covered in MA779 and MA780 (Probability Theory I and II).
- PhD Exam in Mathematical Statistics: This exam covers material covered in MA781 (Estimation Theory) and MA782 (Hypothesis Testing).
- PhD Exam in Applied Statistics: This exam covers the same material as the M.A. Applied exam and is offered at the same time, except that in order to pass it at the PhD level a student must correctly solve all four problems.
Note: Students concentrating in probability may choose to do so either through the statistics track or through the mathematics track. If a student wishes to do so through the mathematics track, the course and exam requirements are different. Details are available here .
Dissertation The dissertation is the major requirement for a Ph.D. student. After the student has completed all course work, the Director of Graduate Studies, in consultation with the student, selects a three-member dissertation committee. One member of this committee is designated by the Director of Graduate Studies as the Major Advisor for the student. Once completed, the dissertation must be defended in an oral examination conducted by at least five members of the Department.
The Dissertation and Final Oral Examination follows the GRS General Requirements for the Doctor of Philosophy Degree .
Satisfactory Progress Toward the Degree Upon entering the graduate program, each student should consult the Director of Graduate Studies (Prof. David Rohrlich) and the Associate Director of the Program in Statistics (Prof. Konstantinos Spiliopoulos). Initially, the Associate Director of the Program in Statistics will serve as the default advisor to the student. Eventually the student’s advisor will be determined in conjunction with their dissertation research. The Associate Director of the Program in Statistics, who will be able to guide the student through the course selection and possible directed study, should be consulted often, as should the Director of Graduate Studies. Indeed, the Department considers it important that each student progress in a timely manner toward the degree. Each M.A. student must have completed the examination by the end of their second year in the program, while a Ph.D. student must have completed the qualifying examination by the third year. Students entering the Ph.D. program with an M.A. degree must have completed the qualifying examination by October of the second year. Failure to meet these deadlines may jeopardize financial aid. Some flexibility in the deadlines is possible upon petition to the graduate committee in cases of inadequate preparation.
Students enrolled in the Graduate School of Arts & Sciences (GRS) are expected to adhere to a number of policies at the university, college, and departmental levels. View the policies on the Academic Bulletin and GRS website .
Residency Post-BA students must complete all of the requirements for a Ph.D. within seven years of enrolling in the program and post-MA students must complete all requirements within five years. This total time limit is set by the Graduate School. Students needing extra time must petition the Graduate School. Also, financial aid is not guaranteed after the student’s fifth year in the program.
Financial Aid
As with all Ph.D. students in the Department of Mathematics and Statistics, the main source of financial aid for graduate students studying statistics is a Teaching Fellowship. These awards carry a stipend as well as tuition remission for six courses per year. Teaching Fellows are required to assist a faculty member who is teaching a course, usually a large lecture section of an introductory statistics course. Generally, the Teaching Fellow is responsible for conducting a number of discussion sections consisting of approximately twenty-five students each, as well as for holding office hours and assisting with grading. The Teaching Fellowship usually entails about twenty hours of work per week. For that reason, Teaching Fellows enroll in at most three courses per semester. A Teaching Fellow Seminar is conducted to help new Teaching Fellows develop as instructors and to promote the continuing development of experienced Teaching Fellows.
Other sources of financial aid include University Fellowships and Research Assistantships. The University Fellowships are one-year awards for outstanding students and are service-free. They carry stipends plus full tuition remission. Students do not need to apply for these fellowships. Research Assistantships are linked to research done with individual faculty, and are paid for through those faculty members’ grants. As a result, except on rare occasions, Research Assistantships typically are awarded to students in their second year and beyond, after student and faculty have had sufficient time to determine mutuality of their research interests.
Regular reviews of the performance of Teaching Fellows and Research Assistants in their duties as well as their course work are conducted by members of the Department’s Graduate Committee.
Ph.D. Program Milestones
The department considers it essential that each student progress in a timely manner toward completion of the degree. The following are the deadlines for achieving the milestones described in the Degree Requirements and constitute the basis for evaluating satisfactory progress towards the Ph.D. These deadlines are not to be construed as expected times to complete the various milestones, but rather as upper bounds. In other words, a student in good standing expecting to complete the degree within the five years of guaranteed funding will meet these milestones by the much e arlier target dates indicated below. Failure to achieve these milestones in a timely manner may affect financial aid.
- Target: April of Year 1
- Deadline: April of Year 2
- Target: Spring of Year 2 post-BA/Spring of Year 1 post-MA
- Deadline: End of Year 3 post-BA/Fall of Year 2 post-MA
- Target: Spring of Year 2
- Deadline: End of Year 3
- Target: Spring of Year 2 or Fall of Year 3 post-BA/October of Year 2 post-MA
- Deadline: End of Year 3 post-BA/October of Year 2 post-MA
- Target: end of Year 3
- Deadline: End of Year 4
- Target: End of Year 5
- Deadline: End of Year 6
If you have any questions regarding our PhD program in Statistics, please reach out to us at [email protected]
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PhD in Statistics
The STEM-designated PhD in Statistics program provides advanced training in topics including probability, linear models, time series analysis, Bayesian statistics, inference, reliability, statistics in law and regulatory policy and much more.
Nearly all GW statistics PhD graduates have secured job placements in the statistics or data science industry, with employers including Amazon, Facebook and Capital One. During the program, PhD students work closely with faculty on original research in their area of interest.
The degree provides training in theory and applications and is suitable for both full-time and part-time students. Most graduate courses are offered in the early evening to accommodate student schedules.
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Graduate Program Advisors
Application Requirements
Prospective PhD students typically have earned a master’s degree in statistics or a related discipline. Students need a strong background in mathematics, including courses in advanced calculus, linear algebra and mathematical statistics.
Complete Application Requirements
"GW encouraged me to tap into expertise from within as well as outside the university while researching my dissertation topic. I learned about the value of collaboration throughout my doctoral studies. Collaboration is so important in science, and it’s been instrumental in our success at Emmes."
Anne Lindblad PhD ’90 President, The Emmes Company
Students in their first semester of the PhD in Statistics program must meet with the program director prior to signing up for classes. Students should continue to seek advice from the advisor throughout the program, particularly when determining whether any previous coursework can be applied toward their degree.
General Examinations
The general examination consists of two parts: a qualifying examination and an examination to determine the student's readiness to carry out the proposed dissertation research.
Each PhD candidate is required to take and pass the PhD qualifying exam. The written exam is given at the beginning of the fall semester each year. It consists of two papers:
- Inference: STAT 6202 and 8263
- Probability: STAT 6201 and 8257
The written exam is required for the first attempt. If a student cannot pass it, then there are two options for the second attempt.
- Option #1 for the second attempt : after approximately a year, the student will retake the written exam (see above for exam description).
- Option #2 for the second attempt : within approximately half a year, based on the scope of the written exam (see above for exam description), the student must demonstrate satisfactory improvements through (open-book, take-home) problem solving and an oral exam (with questions and answers).
No more than two attempts are permitted.
After passing the qualifying examination, the candidate should select a dissertation advisor. In consultation with the advisor, the candidate should pass a readiness examination, usually consisting of a research proposal and an oral examination. A committee of at least two professors should administer the readiness examination.
Dissertation
Students are required to complete a written dissertation that should be defended before an examination committee of at least four examiners. The dissertation should contain original scholarly research and must comply with all other GW rules and regulations. For more guidance on dissertation process, review the CCAS PhD Student Handbook . For formatting and submission guidelines, visit the Electronic Theses and Dissertations Submission website .
Past Theses
Course Requirements
The program requires 72 credit hours, of which at least 48 must be from coursework and at least 12 must be from dissertation research. Up to 24 credit hours may be transferred from a prior master’s degree (contrary to general GW doctoral program requirements , which allow up to 30 transfer credit hours).
| Code | Title | Credits |
|---|---|---|
| Required | ||
| STAT 6201 | Mathematical Statistics I | |
| STAT 6202 | Mathematical Statistics II | |
| STAT 6223 | Bayesian Statistics: Theory and Applications | |
| STAT 8257 | Probability | |
| STAT 8258 | Distribution Theory | |
| STAT 8263 | Advanced Statistical Theory I | |
| STAT 8264 | Advanced Statistical Theory II | |
| At least two of the following: | ||
| STAT 6218 | Linear Models | |
| STAT 8226 | Advanced Biostatistical Methods | |
| STAT 8259 | Advanced Probability | |
| STAT 8262 | Nonparametric Inference | |
| STAT 8265 | Multivariate Analysis | |
| STAT 8273 | Stochastic Processes I | |
| STAT 8274 | Stochastic Processes II | |
| STAT 8281 | Advanced Time Series Analysis | |
| A minimum of 21 additional credits as determined by consultation with the departmental doctoral committee | ||
| The General Examination, consisting of two parts: | ||
| A. A written qualifying examination that must be taken within 24 months from the date of enrollment in the program and is based on: | ||
| STAT 6201 | Mathematical Statistics I | |
| STAT 6202 | Mathematical Statistics II | |
| STAT 8257 | Probability | |
| STAT 8263 | Advanced Statistical Theory I | |
| B. An examination to determine the student’s readiness to carry out the proposed dissertation research | ||
| A dissertation demonstrating the candidate’s ability to do original research in one area of probability or statistics. | ||
PhD Dissertations
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| The commercial activity of nonprofit human service organizations analysis approach: latent class growth analysis approach | Ph. D. | 05/2019 |
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| Psychosocial stress among pregnant women in Puerto Rico | Ph. D. | 05/2019 |
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| Loneliness and self-efficacy: an online mindfulness-based stress reduction intervention for older adults with cardiovascular disease | Ph. D. | 08/2019 |
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| The demographics, behavioral theory, and physical activity patterns of Pokémon Go players | Ph. D. | 05/2019 |
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| Selected topics in network data analysis and phylogenetics | Ph. D. | 05/2019 |
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| Statistical learning methods for group analysis in fMRI data | Ph. D. | 08/2019 |
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| Using functional data analysis to model person responses | Ph. D. | 08/2019 |
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| Sampling for streaming data | Ph. D. | 05/2019 |
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| Symbolic data analysis : statistical inference on interval-valued data regression | Ph. D. | 08/2018 |
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| Campus housing departments as learning organizations: assessing learning culture and organizational performance | Ph. D. | 08/2018 |
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| The impact of extracurricular activity involvement on dropout rates for students with emotional and behavioral disorders | Ph. D. | 05/2018 |
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| Rural student experiences at an R1 doctoral university | Ph. D. | 05/2018 |
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| A comprehensive evaluation of fit statistics to identify the correct item response process | Ph. D. | 12/2018 |
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| Defining preconception wellness and examining its association with preterm birth and its clinical subtypes among Georgia mothers | Ph. D. | 05/2018 |
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| Molecular evolution of chemical-differentially modified biopolymers | Ph. D. | 12/2018 |
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| Dimension reduction and multisource fusion for big data with applications in bioinformatics | Ph. D. | 05/2018 |
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| Statistical inference and learning for topological data analysis | Ph. D. | 08/2018 |
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| Model-based clustering with application of copulas for symbolic data | Ph. D. | 08/2018 |
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| Probabilistic topic modeling based framework for exploration of rdf data | Ph. D. | 08/2018 |
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| Prospective mathematics teachers? noticing of students? statistical reasoning | Ph. D. | 12/2018 |
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| Nonparametric methods for big and complex datasets under a reproducing kernel Hilbert space framework | Ph. D. | 05/2018 |
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| Statistical methods with applications in epigenomics, metagenomics and neuroimaging | Ph. D. | 05/2018 |
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| Composite nanoparticles for biomedical applications | Ph. D. | 05/2018 |
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| Optimal designs for the panel mixed logit model | Ph. D. | 05/2018 |
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| Toward equity in educational planning: power and influence among men and women faculty in Saudi universities | Ph. D. | 05/2017 |
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| Bayesian empirical likelihood for linear regression and penalized regression | Ph. D. | 08/2017 |
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| An exploratory study: : programs foster care students' knowledge of college campus support | Ph. D. | 12/2017 |
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| Towards understanding the interplay between cellular stresses and cancer development | Ph. D. | 08/2017 |
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| Optimizing parameters for qtl mapping study designs using next generation sequencing data | Ph. D. | 05/2017 |
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| Optimal p-value weighting with independent information | Ph. D. | 12/2017 |
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| Bayesian framework for developing and evaluating medical screening tests for early disease detection with applications in oncology | Ph. D. | 05/2017 |
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| A nonparametric method for assessing model-data fit in rasch measurement theory | Ph. D. | 05/2017 |
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| Using learning analytics to investigate self-regulated learning in an asynchronous online course | Ph. D. | 05/2017 |
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| Undergraduate students' informal notions of variability | Ph. D. | 05/2017 |
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| The logic of identity-focused organizational change:: research based on the combined federal campaign | Ph. D. | 08/2017 |
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| Investigating undergraduate student understanding of graphical displays of quantitative data through machine learning algorithms | Ph. D. | 05/2017 |
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| Structural relationships among explanatory style, acculturation, coping strategies, and job satisfaction of foreign-born asian faculty in the u.s. | Ph. D. | 08/2017 |
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| The impact of environmental factors on the production of english narratives by Spanish-English bilingual children | Ph. D. | 08/2016 |
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| Nonlinear constrained optimization in R and its application for sufficient dimension reduction and variable selection | Ph. D. | 08/2016 |
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| Multiple perspectives on the transition from secondary to postsecondary mathematics courses | Ph. D. | 12/2016 |
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| A waiting time approach for a disability model | Ph. D. | 05/2016 |
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| Cluster analysis for symbolic interval data using linear regression method | Ph. D. | 05/2016 |
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| Topics in zero-inflated count regression: coefficients of determination and marginal models | Ph. D. | 08/2016 |
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| Grouped variable screening for ultrahigh dimensional data under linear model | Ph. D. | 05/2016 |
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| An examination of legal issues concerning international and immigrant students in the U.S. | Ph. D. | 08/2016 |
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| Identifying and understanding repetitive patterns in prokaryotic DNA sequences | Ph. D. | 12/2016 |
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| Estimating the effect of magnitude of exposure to developmental education on college student outcomes across sectors: a dose-response approach using two nationally representative student samples | Ph. D. | 08/2016 |
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| Regularized aggregation approaches for complex data | Ph. D. | 12/2016 |
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| Sufficient dimension folding, variable selection, and its inference | Ph. D. | 05/2016 |
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| A Birth and Death Model for RNA-Seq Data Analysis | Ph. D. | 05/2016 |
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| A probabilistic model for gene family evolution | Ph. D. | 05/2016 |
|
| Discovering a regulatory network topology by Markov Chain Monte-Carlo on GPGPUs with special reference to the biological clock of Neurospora crassa | Ph. D. | 05/2015 |
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| The economic affect on teacher efficacy | Ph. D. | 08/2015 |
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| Composite empirical likelihood: a derivation of multiple non-parametric likelihoods | Ph. D. | 05/2015 |
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| Prioritizing hypothesis tests for high throughput data with multiple testing methods | Ph. D. | 12/2015 |
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| Developments of regularized approaches in non-standard paradigm | Ph. D. | 05/2015 |
|
| The effect of motivation and learning anxiety on achievement by modeling problem solving skills and using open educational resources | Ph. D. | 08/2015 |
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| High school mathematics teachers' knowledge and views of conditional probability | Ph. D. | 08/2015 |
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| Sufficient dimension reduction and variable selection via distance covariance | Ph. D. | 05/2015 |
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| Optimal designs for generalized linear models | Ph. D. | 05/2015 |
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| High/ultra-high dimensional single-index models | Ph. D. | 05/2015 |
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| Estimating equations for fitting linear regression models describing the impact of partially-observed time-varying predictors on patterns of weight change among participants seeking to lose weight using ecological momentary assessment data | Ph. D. | 12/2015 |
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| Stable and pseudo sufficient dimension reduction | Ph. D. | 05/2015 |
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| Time series clustering using copula-based higher order Markov process | Ph. D. | 12/2015 |
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| Initial development and validation of the Teacher Empathy Scale: TES | Ph. D. | 05/2014 |
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| Hierarchical Bayesian methods for survey sampling and other applications | Ph. D. | 08/2014 |
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| Symbolic data regression and clustering methods | Ph. D. | 08/2014 |
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| Impact of nocturnal low-level jets on surface turbulence and fluxes | Ph. D. | 05/2014 |
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| On dimension reduction and feature selection in high dimensions | Ph. D. | 05/2014 |
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| The effect of motivation and attitude towards statistics on conceptual understanding of statistics | Ph. D. | 08/2014 |
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| Optimal design of experiments | Ph. D. | 12/2014 |
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| Effects of socio-environmental variability and uncertainty in decisions about fishing effort of a small-scale tuna fishery in Ende, eastern Indonesia | Ph. D. | 05/2014 |
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| Development of nano-based biosensors for the detection and differentiation of bacterial pathogens using surface enhanced Raman spectroscopy | Ph. D. | 12/2014 |
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| Design and analysis issues in high dimension, low sample size problems | Ph. D. | 08/2014 |
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| Matrix time series analysis | Ph. D. | 05/2014 |
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| Generalized quasi-likelihood ratio test for semiparametric analysis of covariance models in longitudinal data | Ph. D. | 12/2014 |
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| Analysis of univariate and multivariate longitudinal data with censored and missing response with complex covariance structure | Ph. D. | 08/2014 |
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| Automaticity and the learning of mathematics | Ph. D. | 12/2014 |
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| Hilbert-Schmidt Independence Criterion in sufficient dimension reduction and feature screening | Ph. D. | 05/2014 |
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| The application of the total evidence approach for phylogenetic reconstruction of selected monothalamous foraminifera of Sapelo Island, Georgia, USA | Ph. D. | 12/2013 |
|
| Extreme value estimators for various non-negative time series with heavy-tail innovations | Ph. D. | 05/2013 |
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| Bayesian multiple testing under dependence with application to functional magnetic resonance imaging | Ph. D. | 08/2013 |
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| Factors that influence the retention of secondary urban science teachers | Ph. D. | 05/2013 |
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| Mixture Poisson point process: assessing heterogeneity in EMA analysis | Ph. D. | 05/2013 |
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| Functional magnetic resonance imaging data clustering | Ph. D. | 12/2013 |
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| Sample integrity in high dimensional data | Ph. D. | 12/2013 |
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| Sample size determination in multi-class classification and prediction based on single-nucleotide polymorphisms | Ph. D. | 08/2013 |
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| Conceptualizing mathematical authority with technology | Ph. D. | 05/2013 |
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| Bayesian factor analysis for fMRI data | Ph. D. | 08/2013 |
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| Essays on corporate finance and employee compensation | Ph. D. | 05/2013 |
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| Locally optimal designs for generalized linear models with a single-variable quadratic polynomial predictor | Ph. D. | 12/2013 |
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| The relationship between teacher evaluation scores and student achievement in English and mathematics: evidence from Pakistan | Ph. D. | 12/2012 |
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| Posterior predictive model checking for the diagnostic input noisy and gate model | Ph. D. | 08/2012 |
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| Numerical optimization and Empty Set Problem in application of empirical likelihood methods | Ph. D. | 12/2012 |
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| Increasing healthy eating behaviors among college students | Ph. D. | 12/2012 |
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| The role of simulation in secondary students' reasoning about probability distributions | Ph. D. | 05/2012 |
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| Secondary work-based learning students' perceptions of their course and work and career-related issues | Ph. D. | 12/2012 |
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| Spatial pattern of brown rot symptoms and fine-scale genetic structure of Monilinia fructicola within stone fruit tree canopies | Ph. D. | 08/2012 |
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| Nonparametric analysis of time series with complex features | Ph. D. | 08/2012 |
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| A descriptive study of educators' gifted referral efficacy using Frasier?s TABs in a predominately African American rural Alabama school district | Ph. D. | 08/2012 |
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| Secondary traumatic stress in military healthcare providers: an examination into empathy and emotional separation as moderating variables | Ph. D. | 05/2012 |
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| Social work practice with trauma survivors: investigating risk and protective factors for secondary traumatic stress in a national sample of social workers | Ph. D. | 08/2012 |
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| Intrinsic motivations of older adult learners in Taiwan | Ph. D. | 05/2012 |
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| Complex systems data mining and knowledge extraction | Ph. D. | 12/2012 |
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| Asymptotic expansions of processes with extreme value random variable innovations | Ph. D. | 08/2012 |
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| The internationalization of U.S. higher education: perceptions from international educators and senior international officers | Ph. D. | 12/2012 |
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| Applications of empirical likelihood to nonresponse problem and changepoint detection | Ph. D. | 12/2012 |
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| Sufficient dimension folding theory and methods | Ph. D. | 12/2012 |
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| Measuring reported learning from supervised practice experiences of graduates of master?s programs in student affairs: the CAS supervised practice study | Ph. D. | 05/2012 |
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| A sign of the times: African American seminarians' attitudes, perceptions and knowledge about HIV and mutual HIV testing in intimate relationships | Ph. D. | 12/2011 |
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| Educator effect on student achievement on Georgia high school economics End-of-Course Test | Ph. D. | 05/2011 |
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| Extinction in the Finnish Daphnia magna metapopulation | Ph. D. | 08/2011 |
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| The impact of precollege characteristics and community college factors on the academic and social adjustment of adult vertical transfer students | Ph. D. | 12/2011 |
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| Perceived readiness of Jamaican community college students for postcollege goals | Ph. D. | 08/2011 |
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| Lexical aspect and lexical saliency in acquisition of past tense-aspect morphology among Ibibio ESL learners | Ph. D. | 12/2011 |
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| Concepts, ideas, visions: thematic characteristics of strategic plans among elite, international universities | Ph. D. | 12/2011 |
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| Pharmacokinetics of anti-HIV agents in rodents | Ph. D. | 08/2010 |
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| Assessment practices of mathematics teachers who also teach AP statistics | Ph. D. | 12/2010 |
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| Effects of career-technical and college-preparatory high school curricula on educational attainment | Ph. D. | 08/2010 |
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| L2E estimation for finite mixture of regression models with applications and L2E with penalty and non-normal mixtures | Ph. D. | 08/2010 |
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| Comparing statistically pooled brain maps in FMRI studies using parametric and non-parametric methods | Ph. D. | 12/2010 |
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| Examining the potential of particle swarm optimization for spatial forest planning and developing a solution quality index for heuristic techniques | Ph. D. | 08/2010 |
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| Symbolic data analysis: interval-valued data regression | Ph. D. | 08/2010 |
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| Emerging principles of ecological network dynamics: innovative synthesis, modeling, analysis, and results | Ph. D. | 12/2010 |
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| What do parents say about childhood obesity?: the development and validation of a survey instrument to measure parent's perceptions of solutions to childhood obesity | Ph. D. | 08/2009 |
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| Correlates of mathematics anxiety among African American high school juniors | Ph. D. | 05/2009 |
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| Congregational social services: leaders' perceptions and experiences | Ph. D. | 08/2009 |
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| Optimal experimental designs for event-related functional magnetic resonance imaging | Ph. D. | 08/2009 |
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| Dissimilarity measures for histogram-valued data and divisive clustering of symbolic objects | Ph. D. | 08/2009 |
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| Rapid techniques for screening wood properties in forest plantations | Ph. D. | 05/2009 |
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| Random effects in point processes: adding flexibility to Ecological Momentary Assessment analysis | Ph. D. | 08/2009 |
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| L2E estimation of mixture complexity | Ph. D. | 08/2009 |
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| Statistical inferences and visualization based on a scale-space approach | Ph. D. | 12/2009 |
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| Sufficient dimension reduction and sufficient variable selection | Ph. D. | 05/2009 |
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| Post-secondary science students? conceptions of randomness and entropy | Ph. D. | 12/2009 |
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| An examination of chief student affairs officers' (CSAO) perceptions of professional competencies | Ph. D. | 08/2009 |
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| Semiparametric zero-inflated regression models: estimation and inference | Ph. D. | 12/2009 |
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| Effect of common errors in microsatellite data on estimates of population differentiation and inferring genotypic structure of complex disease loci using genome-wide expression data | Ph. D. | 08/2008 |
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| Applications of Empirical Likelihood to quantile estimation and longitudinal data | Ph. D. | 08/2008 |
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| Construction and analysis of the University of Georgia Tobacco Documents Corpus | Ph. D. | 05/2008 |
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| Impact of mentorship programs on African-American male high school students' perceptions of engineering | Ph. D. | 08/2008 |
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| Geosimulations of urban growth, dasymetric mapping and population dynamics in northwest Florida, 1974 - 2025 | Ph. D. | 12/2008 |
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| Principal component analysis for interval-valued and histogram-valued data and likelihood functions and some maximum likelihood estimators for symbolic data | Ph. D. | 12/2008 |
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| Statistical issues on mass spectrometry-based protein identification and quantitation | Ph. D. | 12/2008 |
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| Seasonal patterns of perinatal anomalies | Ph. D. | 08/2008 |
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| Variable selection methods with applications | Ph. D. | 12/2008 |
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| A genetical genomics approach to genome scans for complex traits | Ph. D. | 12/2008 |
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| Construction of high-resolution likelihood-based integrated genetic and physical map of Neurospora crassa | Ph. D. | 08/2008 |
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| Geostatistical methods for spatio-temporal analysis of fMRI data | Ph. D. | 08/2008 |
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| Teachers' understanding of students' conceptions about chance: an expert-novice contrast | Ph. D. | 08/2008 |
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| Categorical time series | Ph. D. | 08/2008 |
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| Inference for controlled branching process, Bayesian inference for zero-inflated count data and Bayesian techniques for hairline fracture detection and reconstruction | Ph. D. | 05/2007 |
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| Estimation of the seed dispersal distribution with genotypic data | Ph. D. | 12/2007 |
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| Ownership, governance, & firm performance: private equity in retrospect and prospect | Ph. D. | 05/2007 |
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| Models with subject by treatment and subject by carryover interactions and use of baseline measurements in crossover trials | Ph. D. | 05/2007 |
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| Why do beginning teachers leave school?: pre-service and beginning science teachers' professional dientity and its relation to dropping out of the profession | Ph. D. | 08/2007 |
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| Multivariate association and dimension reduction | Ph. D. | 08/2007 |
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| Gene expression data analysis: applications to disease diagnosis and feed supplementation | Ph. D. | 12/2007 |
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| Liberal arts graduates' college experiences and work preparation | Ph. D. | 08/2007 |
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| Dimension reduction in time series | Ph. D. | 08/2007 |
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| Ecological systems of the young gifted learner | Ph. D. | 08/2007 |
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| An integrated GIS-spatial analysis of Atlanta's urban structure and urban space | Ph. D. | 08/2007 |
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| Spatial differences in food consumption behavior in Uganda | Ph. D. | 08/2007 |
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| Comparison of the dynamic indicators of basic early literacy skills to the comprehensive test of phonological processing and implications for construct validity | Ph. D. | 08/2007 |
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| Applications of smoothly varying functions and tail index estimation | Ph. D. | 08/2007 |
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| A distributional analysis of rural Colorado English | Ph. D. | 08/2006 |
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| Novel nonparametric methods for event time data | Ph. D. | 05/2006 |
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| Patterns of language use in mania | Ph. D. | 08/2006 |
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| Evaluating educational innovations: grant recipients' beliefs and behaviors | Ph. D. | 12/2006 |
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| Maximum likelihood based estimation of hazard function under shape restrictions and related statistical inference | Ph. D. | 05/2006 |
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| Fine spatial resolution forest inventory for Georgia: remote sensing based geostatistical modeling and K nearest neighbor method | Ph. D. | 12/2006 |
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| Robust estimation and inference in finite mixtures of generalized linear models | Ph. D. | 05/2006 |
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| Evaluation of an HIV prevention intervention: the effect of internalized homophobia on outcomes | Ph. D. | 05/2006 |
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| Derecho-producing convective systems in the United States: an assessment of hazards and family formation | Ph. D. | 05/2005 |
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| Politics, public opinion, and privatization: assessing the calculus of consent for market reforms in developed market economies and transition economies | Ph. D. | 08/2005 |
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| Politics, public opinion, and privatization: assessing the calculus of consent for market reforms in developed market economies and transition economies | Ph. D. | 08/2005 |
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| Calibration and validation of the Body Self-Image Questionnaire using the Rasch analysis | Ph. D. | 08/2005 |
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| A study of the resiliency characteristics and proactive behaviors of mothers who have children with short stature | Ph. D. | 08/2005 |
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| Genetic diversity and realized dispersal in the dioecious neotropical tree, Simarouba amara | Ph. D. | 05/2005 |
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| Semiparametric ANCOVA using shape restrictions | Ph. D. | 05/2005 |
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| Empirical and theoretical analyses of the applicability of projection models and mixed models to forest inventory updates | Ph. D. | 08/2005 |
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| Robust estimation in mixture models and small area estimation using cross-sectional time series models | Ph. D. | 08/2005 |
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| The impact of poorly translated items on measurement invariance: a cross-cultural study using mean and covariance structure and differential item functioning | Ph. D. | 08/2005 |
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| Some stochastic shape applications in time series and Markov chains | Ph. D. | 08/2005 |
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| Inference for time series models for count data | Ph. D. | 08/2005 |
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| An examination of outsourced marketing in Division I intercollegiate athletics | Ph. D. | 08/2005 |
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| Black and white attorneys' perspectives on race, the legal system, and continuing legal education: a critical race theory analysis | Ph. D. | 05/2004 |
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| Modelling and analysis of Intraocular Pressure (IOP) data | Ph. D. | 08/2004 |
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| Robust inference for randomized play the winner design | Ph. D. | 08/2004 |
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| Understanding the diagnosis phenomenon of new professionals in student affairs | Ph. D. | 05/2004 |
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| An exploratory study of faculty and student affairs perceptions of undergraduate learning goals at small liberal arts and large research institutions | Ph. D. | 05/2004 |
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| Linear regression under multiple changepoints | Ph. D. | 08/2004 |
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| Genetics of heat tolerance for days open in US Holsteins | Ph. D. | 05/2004 |
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| Parameter estimation for mixtures of generalized linear mixed-effects models | Ph. D. | 05/2004 |
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| Nonparametric Bayesian inference in biostatistics | Ph. D. | 12/2004 |
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| Marginal models for zero-inflated clustered data | Ph. D. | 12/2004 |
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| Bifurcating time series models for cell lineage data | Ph. D. | 05/2004 |
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| A phylogenetic evaluation of Callisia Loefl. (Commelinaceae) based on molecular data | Ph. D. | 12/2003 |
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| An examination of personality and affective dimensions in women with intractable eating disorders | Ph. D. | 08/2003 |
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| Debriefing in simulation games: an examination of reflection on cognitive and affective learning outcomes | Ph. D. | 08/2003 |
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| The use of a geographic information system (GIS) and satellite remote sensing for small-area mortality analysis | Ph. D. | 12/2003 |
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| Linear trends, periodicities, and extremes | Ph. D. | 08/2003 |
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| Shot noise processes | Ph. D. | 08/2003 |
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| An empirical analysis of motives for termination of defined benefit pension plans | Ph. D. | 05/2002 |
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| Household research at the Late Mississippian Little Eegypt site (9MU102) | Ph. D. | 08/2002 |
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| Inference for a class of periodic time series models and their applications | Ph. D. | 05/2002 |
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| Exploring systematic variation in claims of threa : a time-series analysis, 1870 to 1900 | Ph. D. | 12/2002 |
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- PhD in Accounting
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PhD in Econometrics and Statistics
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The Econometrics and Statistics Program provides foundational training in the science of learning from data towards solving business problems. Our students engage in extensive collaborative research on cutting-edge theory in Econometrics, Statistics and Machine Learning as well in applied research from a variety of fields within Booth (such as finance, marketing or economics).
Our program builds on a long tradition of research creativity and excellence at Booth.
Our PhD students become active members of the broad, interdisciplinary and intellectually stimulating Booth community. The program is ideal for students who wish to pursue an (academic) research career in data-rich disciplines, and who are motivated by applications (including but not limited to economics and business). As our PhD student, you will have a freedom to customize your program by combining courses at Booth (including business-specific areas such as marketing, finance or economics) with offerings at our partnering departments at the University of Chicago (Department of Statistics and Kenneth C. Griffin Department of Economics). You will acquire a solid foundation in both theory and practice (including learning theory, Bayesian statistics, causal inference or empirical asset pricing).
Our Distinguished Econometrics and Statistics Faculty
Chicago Booth’s Econometrics and Statistics faculty are committed to building strong collaborative relationships with doctoral students. We serve as research advisors and career mentors. Major areas of departmental research include: learning theory; causal inference; machine learning; Bayesian inference; decision theory; graphical models; high dimensional inference; and financial econometrics.
Bryon Aragam
Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar
Nabarun Deb
Assistant Professor of Econometrics and Statistics
Christian B. Hansen
Wallace W. Booth Professor of Econometrics and Statistics
Tetsuya Kaji
Associate Professor of Econometrics and Statistics and Richard Rosett Faculty Fellow
Tengyuan Liang
Professor of Econometrics and Statistics and William Ladany Faculty Fellow
Nicholas Polson
Robert Law, Jr. Professor of Econometrics and Statistics
Veronika Rockova
Professor of Econometrics and Statistics, and James S. Kemper Faculty Scholar
Jeffrey R. Russell
Alper Family Professor of Econometrics and Statistics
Ekaterina (Katja) Smetanina
Assistant Professor of Econometrics and Statistics and Asness Junior Faculty Fellow
Panagiotis Toulis (Panos)
Associate Professor of Econometrics and Statistics, and John E. Jeuck Faculty Fellow
Dacheng Xiu
Professor of Econometrics and Statistics
Scholarly Publications
Our PhD students' research has been published in top journals including Econometrica, Journal of Royal Statistical Society, Journal of Econometrics, Neural Information Processing Systems and Journal of Machine Learning Research. Below is a recent list of publications and working papers authored by our PhD students. Modeling Tail Index with Autoregressive Conditional Pareto Model Zhouyu Shen, Yu Chen and Ruxin Shi, Journal of Business and Economic Statistics, (40) 2022 Online Learning to Transport via the Minimal Selection Principle Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan, Proceedings of 35th Conference on Learning Theory (COLT), (178) 2022 FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting Boxin Zhao, Samuel Wang and Mladen Kolar, Journal of Machine Learning Research, (23) 2022 Approximate Bayesian Computation via Classification Yuexi Wang, Tetsuya Kaji and Veronika Rockova, Journal of Machine Learning Research (In press), 2022 Reversible Gromov-Monge Sampler for Simulation-Based Inference YoonHaeng Hur, Wenxuan Guo and Tengyuan Liang, Journal of the American Statistical Association (R&R). 2021. Data Augmentation for Bayesian Deep Learning Yuexi Wang, Nicholas Polson and Vadim Sokolov, Bayesian Analysis (In press), 2022 Pessimism Meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning Boxiang Lyu, Zhaoran Wang, Mladen Kolar and Zhuoran Yang, In Proceedings of the 39th International Conference on Machine Learning (ICML), (162) 2022 Optimal Estimation of Gaussian DAG Models Ming Gao, Wai Ming Tai and Bryon Aragam, International Conference on Artificial Intelligence and Statistics (AISTATS), (151) 2022 Multivariate Change Point Detection for Heterogeneous Series Yuxuan Guo, Ming Gao, and Xiaoling Lu, Neurocomputing, (510) 2022 Disentangling Autocorrelated Intraday Returns Rui Da and Dacheng Xiu, Journal of Econometrics (R&R), 2021 When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility Rui Da and Dacheng Xiu, Econometrica, (89) 2021 Efficient Bayesian Network Structure Learning via Local Markov Boundary Search Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Structure Learning in Polynomial Time: Greedy Algorithms, Bregman Information, and Exponential Families Goutham Rajendran, Bohdan Kivva, Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Variable Selection with ABC Bayesian Forests Yi Liu, Yuexi Wang and Veronika Rockova, Journal of the Royal Statistical Association: Series B, (83) 2021 A Polynomial-time Algorithm for Learning Non-parametric Causal Graphs Ming Gao, Yi Ding, and Bryon Aragam, Advances in Neural Information Processing System (NeurIPS), (33) 2020 Uncertainty Quantification for Sparse Deep Learning Yuexi Wang and Veronika Rockova, International Conference on Artificial Intelligence and Statistics (AISTATS), (2018) 2020 Direct Estimation of Differential Functional Graphical Models Boxin Zhao, Samuel Wang and Mladen Kolar, Advances in neural information processing systems (NeurIPS), (32) 2019
The Effects of Economic Uncertainty on Financial Volatility: A Comprehensive Investigation Chen Tong, Zhuo Huang, Tianyi Wang, and Cong Zhang, Journal of Empirical Finance (R&R), 2022
Spotlight on Research
Econometrics and statistics research from our PhD students and faculty is often featured in the pages of Chicago Booth Review.
Is There a Ceiling for Gains in Machine-Learned Arbitrage?
In a recent paper by Chicago Booth’s Stefan Nagel and Dacheng Xiu and Booth PhD student Rui Da, findings suggest that there are limits to statistical arbitrage investment.
How (In)accurate Is Machine Learning?
Three Chicago Booth researchers quantify the likelihood of machine learning leading business executives astray.
Would You Trust a Machine to Pick a Vaccine?
"If we understand why a black-box method works, we can trust it more with our decisions, explains [Booth's] Ročková, one of the researchers trying to narrow the gap between what’s done in practice and what’s known in theory. "
A Network of Support
Booth’s Econometrics and Statistics group has been partnering with several (data science and interdisciplinary) research centers and institutes that facilitate the translation of research into practice. Through these venues, our PhD students can foster a strong research community and find additional resources including elective courses, funding for collaborative student work, and seminars with world-renowned scholars.
Data Science Institute at the University of Chicago The Data Science Institute executes the University of Chicago’s bold, innovative vision of Data Science as a new discipline by advancing interdisciplinary research, partnerships with industry, government, and social impact organizations. Center for Applied Artificial Intelligence The Center for Applied AI incubates transformative projects that fundamentally shape how humans use AI to interact with each other and the world. The Center’s innovative research uses machine learning and behavioral science to investigate how AI can best be used to support human decision-making and improve society. Toyota Technological Institute at Chicago Toyota Technological Institute at Chicago (TTIC) is a philanthropically endowed academic computer science institute, dedicated to basic research and graduate education in computer science. Its mission is to achieve international impact through world-class research and education in fundamental computer science and information technology. The Institute is distinctive to the American educational scene in its unique combination of graduate education and endowed research.
The Becker Friedman Institute for Economics With a mission of turning evidence-based research into real-world impact, the Becker Friedman Institute brings together the University of Chicago’s economic community. Ideas are translated into accessible formats and shared with world leaders tasked with solving pressing economic problems. Committee on Quantitative Methods in Social, Behavioral and Health Sciences This is an interdisciplinary community of faculty and students interested in methodological research in relation to applications in social, behavioral, and health sciences. The goals are to create an intellectual niche, exchange research ideas, facilitate research collaborations, share teaching resources, enhance the training of students, and generate a collective impact on the University of Chicago campus and beyond. The Institute for Data, Econometrics, Algorithms, and Learning The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments.
The Fama-Miller Center for Research in Finance Tasked with pushing the boundaries of research in finance, the Fama-Miller Center provides institutional structure and support for researchers in the field. James M. Kilts Center for Marketing The Kilts Center facilitates faculty and student research, supports innovations in the marketing curriculum, funds scholarships for MBA and PhD students, and creates engaging programs aimed at enhancing the careers of students and alumni.
Inside the Student Experience
Damian Kozbur, PhD ’14, says PhD students at Booth have the flexibility to work on risky problems that no one else has examined.
Video Transcript
Damian Kozbur, ’14: 00:01 I went to graduate school in order to develop econometrics tools in conjunction with machine-learning tools in conjunction with economic theory in order to do inference for economic parameters. When you work in high dimensional estimation and you're dealing with problems where the number of variables you're looking at can potentially be in the millions, there's no way to visualize what's going on. Demands now really require that you can handle huge datasets. There's something really satisfying about studying a problem and studying it well. I would say Booth is an excellent place to do it. You have the flexibility to work on really risky problems where you're trying to navigate this landscape that nobody's ever really looked at before. You have an opportunity to dig deeper. You have an opportunity to be rigorous. The faculty is there to help you. They're trying to figure out the same kinds of problems. Things that you figure out cannot always be visualized and it cannot always be easily understood. That doesn't necessarily mean that it's not practical or not useful.
Damian Kozbur, ’14: 01:08 There's an incredible explosion in terms of the amount of data we have on everything. There is an incredible explosion in terms of our understanding of high dimensional econometrics. If you're doing innovative work right now, it will have an impact.
Current Econometrics and Statistics Students
PhD students in econometrics and statistics apply statistical methods to a wide range of business problems, from the effectiveness of machine-learning tools to video-game preferences. Our graduates go on to work in high-profile institutions, generally in academia, finance, or data science.
Current Students
Y ifei Chen
Chaoxing Dai
Wenxuan Guo
Shunzhuang Huang So Won (Sowon) Jeong
Jizhou Liu Edoardo Marcelli
Zhouyu Shen
Shengjun (Percy) Zhai
Current Students in Sociology and Business
Jacy Anthis
Program Expectations and Requirements
The Stevens Doctoral Program at Chicago Booth is a full-time program. Students generally complete the majority of coursework and examination requirements within the first two years of studies and begin work on their dissertation during the third year. For details, see General Examination Requirements by Area in the Stevens Program Guidebook below.
Download the 2023-2024 Guidebook!
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Dissertations & Theses
The following is a list of recent statistics and biostatistics PhD Dissertations and Masters Theses.
Jeffrey Gory (2017) PhD Dissertation (Statistics): Marginally Interpretable Generalized Linear Mixed Models Advisors: Peter Craigmile & Steven MacEachern
Yi Lu (2017) PhD Dissertation (Statistics): Function Registration from a Bayesian Perspective Advisors: Radu Herbei & Sebastian Kurtek
Michael Matthews (2017) PhD Dissertation (Statistics): Extending Ranked Sampling in Inferential Procedures Advisor: Douglas Wolfe
Anna Smith (2017) PhD Dissertation (Statistics): Statistical Methodology for Multiple Networks Advisor: Catherine Calder
Weiyi Xie (2017) PhD Dissertation (Statistics): A Geometric Approach to Visualization of Variability in Univariate and Multivariate Functional Data Advisor: Sebastian Kurtek
Jingying Zeng (2017) Masters Thesis (Statistics): Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering Advisors: Matthew Pratola & Laura Kubatko
Han Zhang (2017) PhD Dissertation (Statistics): Detecting Rare Haplotype-Environmental Interaction and Nonlinear Effects of Rare Haplotypes using Bayesian LASSO on Quantitative Traits Advisor: Shili Lin
Mark Burch (2016) PhD Dissertation (Biostatistics): Statistical Methods for Network Epidemic Models Advisor: Grzegorz Rempala
Po-hsu Chen (2016) PhD Dissertation (Statistics): Modeling Multivariate Simulator Outputs with Applications to Prediction and Sequential Pareto Minimization Advisors: Thomas Santner & Angela Dean
Yanan Jia (2016) PhD Dissertation (Statistics): Generalized Bilinear Mixed-Effects Models for Multi-Indexed Multivariate Data Advisor: Catherine Calder
Rong Lu (2016) PhD Dissertation (Biostatistics): Statistical Methods for Functional Genomics Studies Using Observational Data Advisor: Grzegorz Rempala (Public Health)
Junyan Wang (2016) PhD Dissertation (Statistics): Empirical Bayes Model Averaging in the Presence of Model Misfit Advisors: Mario Peruggia & Christopher Hans
Ran Wei (2016) PhD Dissertation (Statistics): On Estimation Problems in Network Sampling Advisors: David Sivakoff & Elizabeth Stasny
Hui Yang (2016) PhD Dissertation (Statistics): Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation Advisors: Elizabeth Stasny & Asuman Turkmen
Matthew Brems (2015) Masters Thesis (Statistis): The Rare Disease Assumption: The Good, The Bad, and The Ugly Advisor: Shili Lin
Linchao Chen (2015) PhD Dissertation (Statistics): Predictive Modeling of Spatio-Temporal Datasets in High Dimensions Advisors: Mark Berliner & Christopher Hans
Casey Davis (2015) PhD Dissertation (Statistics): A Bayesian Approach to Prediction and Variable Selection Using Nonstationary Gaussian Processes Advisors: Christopher Hans & Thomas Santner
Victor Gendre (2015) Masters Thesis (Statistics): Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency Advisor: Radu Herbei
Zhengyu Hu (2015) PhD Dissertation (Statistics): Initializing the EM Algorithm for Data Clustering and Sub-population Detection Advisors: Steven MacEachern & Joseph Verducci
David Kline (2015) PhD Dissertation (Biostatistics): Systematically Missing Subject-Level Data in Longitudinal Research Synthesis Advisors: Eloise Kaizar, Rebecca Andridge (Public Health)
Andrew Landgraf (2015) PhD Dissertation (Statistics): Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters Advisor: Yoonkyung Lee
Andrew Olsen (2015) PhD Dissertation (Statistics): When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods Advisor: Radu Herbei
Elizabeth Petraglia (2015) PhD Dissertation (Statistics): Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS) Advisor: Elizabeth Stasny
Mark Risser (2015) PhD Dissertation (Statistics): Spatially-Varying Covariance Functions for Nonstationary Spatial Process Modeling Advisor: Catherine Calder
John Stettler (2015) PhD Dissertation (Statistics): The Discrete Threshold Regression Model Advisor: Mario Peruggia
Zachary Thomas (2015) PhD Dissertation (Statistics): Bayesian Hierarchical Space-Time Clustering Methods Advisor: Mark Berliner
Sivaranjani Vaidyanathan (2015) PhD Dissertation (Statistics): Bayesian Models for Computer Model Calibration and Prediction Advisor: Mark Berliner
Xiaomu Wang (2015) PhD Dissertation (Statistics): Robust Bayes in Hierarchical Modeling and Empirical Bayes Analysis in Multivariate Estimation Advisor: Mark Berliner
Staci White (2015) PhD Dissertation (Statistics): Quantifying Model Error in Bayesian Parameter Estimation Advisor: Radu Herbei
Jiaqi Zaetz (2015) PhD Dissertation (Statistics): A Riemannian Framework for Shape Analysis of Annotated 3D Objects Advisor: Sebastian Kurtek
Fangyuan Zhang (2015) PhD Dissertation (Biostatistics): Detecting genomic imprinting and maternal effects in family-based association studies Advisor: Shili Lin
Natural Sciences and Mathematics
Mathematical sciences.
PhD Dissertations in Statistics
| Year of Graduation | Student | Supervising Professor | Dissertation Title |
|---|---|---|---|
| 2023 | Tejasv Bedi | Qiwei Li | BAYESIAN MODEL BASED CLUSTER ANALYSIS AND ITS APPLICATIONS IN EPIDEMIOLOGY & MICROBIOLOGY |
| 2023 | Huan Chen | Chuan-Fa Tang | RISK-ASSOCIATED INFERENCES IN SURVIVAL ANALYSIS: A STUDY ON ADEQUACY OF THE COX MODEL AND ISOTONIC PROPORTIONAL HAZARD MODELS |
| 2023 | Kevin Lutz | Qiwei Li | BAYESIAN STATISTICAL METHODS FOR URINARY MICROBIOME DATA ANALYSIS |
| 2023 | Ying Chen | Chuan-Fa Tang | Regularized Estimation for Semi-parametric Multivariate Accelerated Failure Time Model Under Non-Randomized Design |
| 2023 | Lakshika Ruberu | Swati Biswas | SOME CONTRIBUTIONS TO THE RISK PREDICTION OF HEREDITARY BREAST CANCER AND SUBSTANCE USE DISORDERS |
| 2022 | Dhanushka Rajapaksha | Swati Biswas and Pankaj Choudhary | Risk Prediction Models for Substance Use Disorders |
| 2022 | Norah Alyabs | Sy Han Chiou | Comparing Practical Approaches for Regression Models with Censored Covariates |
| 2022 | Qi Guo | Sy Han Chiou | Testing quasi-independence with survival tree approaches |
| 2022 | Tian Jiang | Sam Efromovich | Nonparametric Regression with the Scale Depending on Auxiliary Covariates and Missing Data |
| 2021 | Galappaththige Sajith de Silva | Pankaj Choudhary | Contributions to Functional Data Analysis |
| 2021 | Jiaju Wu | Sam Efromovich | Efficient Nonparametric Spectral Density Estimation with Randomly Censored Time Series |
| 2021 | Qinyi Zhou | Sunyoung Shin and Min Chen | SOME METHODS AND APPLICATIONS OF LARGE-SCALE GENOMIC DATA ANALYSIS |
| 2020 | Akash Roy | Frank Konietschke | THE NONPARAMETRIC BEHRENS-FISHER PROBLEM WITH DEPENDENT REPLICATES |
| 2020 | Cong Zhang | Min Chen and Michael Zhang | TRACKING DISSEMINATION OF PLASMIDS IN THE MURINE GUT USING HI-C SEQUENCING & BAYESIAN LANDMARK-BASED SHAPE ANALYSIS OF TUMOR PATHOLOGY IMAGES |
| 2020 | Dipnil Chakraborty | Sam Efromovich | NONPARAMETRIC REGRESSION FOR RESPONSES MISSING NOT AT RANDOM |
| 2020 | Dorcas Ofori-Boateng | Yulia Gel | FROM SINGLE TO MULTILAYER NETWORKS: UNDERSTANDING NETWORK FUNCTIONALITY THROUGH A TOPOLOGICAL PERSPECTIVE |
| 2020 | Kalupahana Gunawardana | Frank Konietschke | NONPARAMETRIC MULTIPLE COMPARISONS AND SIMULTANEOUS CONFIDENCE INTERVALS FOR GENERAL MULTIVARIATE FACTORIAL DESIGNS |
| 2020 | Marwah Soliman | Yulia Gel | QUANTIFYING ENVIRONMENTAL RISKS USING A FUSION OF STATISTICAL AND MACHINE LEARNING METHODS |
| 2020 | Mohammad Shaha Alam Patwary | Pankaj Choudhary | ESTIMATION OF COVARIANCE STRUCTURES IN FUNCTIONAL MIXED MODELS WITH APPLICATION TO HERITABILITY ESTIMATION |
| 2020 | Xiaochen Yuan | Swati Biswas | BIVARIATE LOGISTIC BAYESIAN LASSO FOR DETECTING RARE HAPLOTYPE ASSOCIATION WITH TWO CORRELATED PHENOTYPES |
| 2020 | Yashi Bu | Min Chen | A BAYESIAN MODELING FOR PAIRED DATA IN GENOME-WIDE ASSOCIATION STUDIES WITH APPLICATION TO BREAST CANCER |
| 2020 | Yu Zhang | Min Chen and Michael Zhang | ESTIMATING DISTRIBUTIONS OF DELETION MUTATIONS AMONG BACTERIAL POPULATIONS AND GRAPHICAL MODELING OF MULTIPLE BIOLOGICAL PATHWAYS IN GENOME-WIDE ASSOCIATION STUDIES |
| 2019 | Asim Dey | Yulia Gel | ROLE OF LOCAL GEOMETRY IN RESILIENCE AND FUNCTIONS OF COMPLEX NETWORKS |
| 2019 | Cong Cao | Frank Konietschke | The Behrens-Fisher Problem in General Factorial Design with Covariates |
| 2019 | Dongfang Zhang | Min Chen | Weighted Least-Squares Semiparametric Accelerated Failure Time Model with Generalized Estimating Equation |
| 2019 | Umar Islambekov | Yulia Gel | UTILITY OF BETTI SEQUENCES AS PERSISTENT HOMOLOGY-BASED TOPOLOGICAL DESCRIPTORS IN APPLICATION TO INFERENCE FOR SPACE-TIME PROCESSES AND TIME SERIES OF COMPLEX NETWORKS |
| 2018 | Chowdhury, Marzana | Swati Biswas and Pankaj Choudhary | Prediction of Individualized Risk of Contralateral Breast Cancer (Won the Best Dissertation Award of the School for Natural Sciences and Mathematics for 2017-18) |
| 2018 | Francis Bilson-Darku | Bhargab Chattopadhyay and Frank Konietschke | Study on Parameter Estimation via Multistage Sampling with Applications |
| 2018 | Jiayi Wu | Sam Efromovich | Wavelet Analysis of Big Data Contaminated by Large Noise in an FMRI Study of Neuroplasticity |
| 2018 | Lak N. K. Rankothgedara | Pankaj Choudhary | Contributions to Modeling and Analysis of Method Comparison Data |
| 2018 | Lei Zhang | Swati Biswas | A Unified Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies |
| 2018 | Xin Huang | Yulia Gel | Robust Analysis of Non-Parametric Space-Time Clustering |
| 2017 | Cheng Wang | Qiongxia Song | Extensions of Semiparametric Single Index Models |
| 2017 | Yahui Tian | Yulia Gel | Nonparametric and Robust Methods for Community Detection in Complex Networks |
| 2016 | Ananda Datta | Swati Biswas | Detecting Rare Haplotype Disease Association: Comparison of Existing Population-Based Methods and a New Family-Based Quantitative Bayesian Lasso Method |
| 2016 | Cesar A. J. Acosta-Mejia | Michael Baron | Pseudolikelihood Methods in Multichannel Change-Point Detection |
| 2016 | Jufen Chu | Sam Efromovich | Nonparametric Hazard Rate Estimation with Left Truncated and Right Censored Data |
| 2016 | Lasitha Rathnayake | Pankaj Choudhary | Modeling and Analysis of Functional and Longitudinal Data with Biomedical Applications |
| 2016 | Yuan Zhang | Swati Biswas | Detecting Rare Haplotype-Environment Interaction Under Uncertainty of Gene-Environment Independence Assumption with an Extension to Complex Sampling Data (Won the Best Dissertation Award of the School for Natural Sciences and Mathematics for 2016-17) |
| 2016 | Yujing Cao | Min Chen | Graphical Modeling of Biological Pathways in Genomic Studies |
| 2016 | Yunfei Wang | Robert Serfling | Connections Among Multivariate Rank Functions, Depth Functions, and Sign and Signed-Rank Statistics |
| 2015 | Ming Chen | Qiongxia Song | High Dimensional and Functional Time Series Analysis with Applications in Finance |
| 2015 | Shanshan Wang | Robert Serfling | Masking and Swamping Robustness of Outlier Detection Procedures |
| 2015 | Tian Zhao | Michael Baron | Multiple Comparisons in Truncated Group Sequential Experiments with Applications in Clinical Trials |
| 2015 | Tiansong Wang | Michael Baron | Multi-Sensor Changepoint Detection |
| 2015 | Uditha Wijesuriya | Robert Serfling | Exploratory Nonparametric Functional Data Analysis Using the Spatial Depth Approach |
| 2014 | Bo Hong | Larry Ammann | Variable Selection for Cluster and Mixture Models |
| 2014 | Ekaterina Smirnova | Sam Efromovich | Large Cross-Covariance Matrix Estimation with Applications to FMRI Data |
| 2014 | Lakshika Nawarathna | Pankaj Choudhary | Heteroscedastic Models for Method Comparison Data |
| 2014 | Marcel Carcea | Robert Serfling | Contributions to Time Series Modeling Under Lower Order Moment Assumptions |
| 2012 | Dishari Sengupta | Pankaj Choudhary | A Robust Linear Mixed Effects Model with Application to Method Comparison Studies |
| 2012 | Gerald Ogola | Robert Serfling | Statistical Methods for the Interpretation of Prostate Cancer Biopsy Results (Won 2nd place in the 2012 Conference of Texas Statisticians Graduate Student Poster Competition) |
| 2012 | Houssein Assaad | Pankaj Choudhary | L-Estimation with Repeated Measurements Data |
| 2012 | Rui Xu | Michael Baron | Sequential Analysis of Credibility and Actuarial Risks |
| 2012 | Seoweon Jin | Sam Efromovich | Nonparametric Confidence Bands for Regression Curves |
| 2012 | Shyamal De | Michael Baron | Simultaneous Testing of Multiple Hypotheses in Sequential Experiments (Won 1 place in the 2012 Conference of Texas Statisticians Graduate Student Poster Competition) |
| 2010 | Jorge Villa-Carillo | Larry Ammann | Topological Overlap Measure of Similarity |
| 2010 | Ke Chen | Sam Efromovich | Density Estimation of Randomly Right Censored Data |
| 2010 | Satyaki Mazumder | Robert Serfling | Affine Invariant, Robust, and Computationally Easy Multivariate Outlier Identification and Related Methods (Won the 2010 President David Daniel Excellent Dissertation Award) |
| 2010 | Shahla Ramachandar | Larry Ammann | Pre-Processing Methods and Stepwise Variable Selection for Binary Classification of High-Dimensional Data |
| 2010 | Xian Yu | Michael Baron and Pankaj Choudhary | Sequential Change-Point Analysis of Markov Chains with Application to Fast Detection of Epidemic Trends |
| 2010 | Yi Zhong | Michael Baron | Optimization of Error Spending and Power Spending in Sequentially Planned Statistical Experiments |
| 2010 | Zibonele Valdez-Jasso | Sam Efromovich | Aggregated Wavelet Estimation with Applications |
| 2009 | Xuan Chen | Michael Baron | Change-Point Analysis of Survival Data with Application in Clinical Trials |
| 2007 | Indra Kshattry | Larry Ammann | Modeling Arsenic in the Wells of Nepal |
| 2007 | Kunshan Yin | Pankaj Choudhary | A Bayesian Paradigm for Method Comparison Studies |
| 2006 | Jingsi Xia | Michael Baron | Optimal Sequentially Planned Change-Point Detection Procedures |
| 2006 | Peng Xiao | Robert Serfling | Contributions to Multivariate L-Moments: L-Comoment Matrices |
| 2006 | Sumihiro Suzuki | Michael Baron | Constructive Methodologies of Optimal Sequential Plans |
| 2005 | Hanzhe Zheng | Larry Ammann | Asymptotic Distributions of Similarity Coefficients and Similarity Tests |
| 2005 | Weihua Zhou | Robert Serfling | Multivariate Spatial U-Quantiles: Theory and Applications |
| 2005 | Xin Dang | Robert Serfling | Nonparametric Multivariate Outlier Detection Methods, with Applications |
| 2003 | Claudia Schmegner | Michael Baron | Decision Theoretic Results for Sequentially Planned Statistical Procedures |
| 2003 | Jin Wang | Robert Serfling | On Nonparametric Multivariate Scale, Kurtosis, and Tailweight Measures |
| 2002 | Ryan Gill | Michael Baron | Introduction to Generalized Broken Line Regressio |
| 2000 | Filemon Ramirez-Perez | Robert Serfling | Contributions to Shot Noise on Cluster Processes with Cluster Marks |
| 2000 | Zhenwu Chen | Robert Serfling | Trimmed and Winsorized M- and Z-Estimators, with Applications to Robust Estimation in Neural Network Models |
| 1999 | Vytaras Brazauskas | Robert Serfling | Robust and Nonparametric Methods for Pareto Tail Index Estimation, with Actuarial Science Applications |
| 1998 | Yijun Zuo | Robert Serfling | Contributions to Theory and Applications of Statistical Depth Functions |
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College of Liberal Arts and Sciences
Department of Statistics
Ph.d. in statistics.
The Doctor of Philosophy (Ph.D.) in Statistics provides students with rigorous training in the theory, methodology, computation, and application of statistics.
View Admissions Requirements
Program Details
UConn statistics Ph.D. students work closely with faculty on advanced research topics over a wide range of theory and application areas. They also engage with an active community of scholars and students who engage with peers on campus and with professional networks beyond UConn.
Through their coursework, mentorship, and community engagement experiences, our students develop diverse skills that allow them to collaborate and innovate with researchers in applied fields. Graduates of our program go on to high profile positions in academia, industry, and government as both statisticians and data scientists.
Academic Requirements
UConn’s Ph.D. in Statistics offers students rigorous training in statistical theories and methodologies, which they can apply to a wide range of academic and professional fields. Starting in their second year, Ph.D. students establish an advisory committee, consisting of a major advisor and two associate advisors. Together they develop an individualized plan of study based on the students career goals and interests.
All Ph.D. students are required to complete:
- A sequence of required core courses and elective courses from another field of study.
- A qualifying examination and general examination.
- A dissertation.
View full degree requirements
Students entering the program with a bachelor’s degree are typically required to take 16 to 18 courses to earn a Ph.D. in Statistics.
Core Courses
The following core courses are required for all Ph.D. students:
- STAT 5545 and 5555. Mathematical Statistics.
- STAT 5505 and 5605. Applied Statistics.
- STAT 5725 and 5735. Linear Models.
- STAT 6315 and 6515. Theory of Statistics.
- STAT 6325 and 6894. Measure Theory and Probability Theory.
- STAT 5515. Design of Experiments.
- STAT 5091. Statistics Internship or
- STAT 5094. Seminars in Statistics.
Each core course carries three credits, except for the one-credit STAT 5091 or 5094, for a total of 34 credits. Additional credits can be earned from the list of elective courses.
Elective Courses
In general, Ph.D. students are required to elect one or two courses from other departments. However, it is sufficient to take one graduate-level course from the Department of Mathematics. Ph.D. students are also encouraged to take courses in computer science and in application areas such as biology or economics. The elective course(s) must be approved by the student’s major advisor.
Under certain circumstances, a major advisor can exempt their student from the above requirement, if the student has had internships or a research assistantship in interdisciplinary areas.
Browse the UConn graduate course catalog.
Financial Aid
The Department expects Ph.D. students to finish their studies within four years. For students arriving without an MS degree in mathematics or statistics, the Department may provide up to five years of financial support. For those arriving with such a degree, the Department may provide up to four years of financial support.
In order to receive continuous support, Ph.D. students should take at at least nine credits per semester until taking the Ph.D. qualifying exam.
Learn more about financial aid
February 1 (early deadline) April 1 (final deadline)
Please apply by February 1 if you wish to be considered for financial aid.
Individuals with a bachelor’s degree in any major, with a background in mathematics and statistics, are encouraged to apply.
International students must consult with UConn International Student and Scholar Services for visa rules and University requirements.
Full Admissions Requirements
Please note: The Department does not offer a joint MS/Ph.D. program. Current UConn students enrolled in a statistics master’s program who wish to pursue the Ph.D. in Statistics must reapply to the Graduate School.
For questions about the Ph.D. in Statistics, please contact:
Vladimir Pozdnyakov
Professor and Director of Graduate Admission [email protected]
Recent PhD Theses
Additional theses can be found on UWSpace .
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Statistical Methods in Theses: Guidelines and Explanations
Signed August 2018 Naseem Al-Aidroos, PhD, Christopher Fiacconi, PhD Deborah Powell, PhD, Harvey Marmurek, PhD, Ian Newby-Clark, PhD, Jeffrey Spence, PhD, David Stanley, PhD, Lana Trick, PhD
Version: 2.00
This document is an organizational aid, and workbook, for students. We encourage students to take this document to meetings with their advisor and committee. This guide should enhance a committee’s ability to assess key areas of a student’s work.
In recent years a number of well-known and apparently well-established findings have failed to replicate , resulting in what is commonly referred to as the replication crisis. The APA Publication Manual 6 th Edition notes that “The essence of the scientific method involves observations that can be repeated and verified by others.” (p. 12). However, a systematic investigation of the replicability of psychology findings published in Science revealed that over half of psychology findings do not replicate (see a related commentary in Nature ). Even more disturbing, a Bayesian reanalysis of the reproducibility project showed that 64% of studies had sample sizes so small that strong evidence for or against the null or alternative hypotheses did not exist. Indeed, Morey and Lakens (2016) concluded that most of psychology is statistically unfalsifiable due to small sample sizes and correspondingly low power (see article ). Our discipline’s reputation is suffering. News of the replication crisis has reached the popular press (e.g., The Atlantic , The Economist , Slate , Last Week Tonight ).
An increasing number of psychologists have responded by promoting new research standards that involve open science and the elimination of Questionable Research Practices . The open science perspective is made manifest in the Transparency and Openness Promotion (TOP) guidelines for journal publications. These guidelines were adopted some time ago by the Association for Psychological Science . More recently, the guidelines were adopted by American Psychological Association journals ( see details ) and journals published by Elsevier ( see details ). It appears likely that, in the very near future, most journals in psychology will be using an open science approach. We strongly advise readers to take a moment to inspect the TOP Guidelines Summary Table .
A key aspect of open science and the TOP guidelines is the sharing of data associated with published research (with respect to medical research, see point #35 in the World Medical Association Declaration of Helsinki ). This practice is viewed widely as highly important. Indeed, open science is recommended by all G7 science ministers . All Tri-Agency grants must include a data-management plan that includes plans for sharing: “ research data resulting from agency funding should normally be preserved in a publicly accessible, secure and curated repository or other platform for discovery and reuse by others.” Moreover, a 2017 editorial published in the New England Journal of Medicine announced that the International Committee of Medical Journal Editors believes there is “an ethical obligation to responsibly share data.” As of this writing, 60% of highly ranked psychology journals require or encourage data sharing .
The increasing importance of demonstrating that findings are replicable is reflected in calls to make replication a requirement for the promotion of faculty (see details in Nature ) and experts in open science are now refereeing applications for tenure and promotion (see details at the Center for Open Science and this article ). Most dramatically, in one instance, a paper resulting from a dissertation was retracted due to misleading findings attributable to Questionable Research Practices. Subsequent to the retraction, the Ohio State University’s Board of Trustees unanimously revoked the PhD of the graduate student who wrote the dissertation ( see details ). Thus, the academic environment is changing and it is important to work toward using new best practices in lieu of older practices—many of which are synonymous with Questionable Research Practices. Doing so should help you avoid later career regrets and subsequent public mea culpas . One way to achieve your research objectives in this new academic environment is to incorporate replications into your research . Replications are becoming more common and there are even websites dedicated to helping students conduct replications (e.g., Psychology Science Accelerator ) and indexing the success of replications (e.g., Curate Science ). You might even consider conducting a replication for your thesis (subject to committee approval).
As early-career researchers, it is important to be aware of the changing academic environment. Senior principal investigators may be reluctant to engage in open science (see this student perspective in a blog post and podcast ) and research on resistance to data sharing indicates that one of the barriers to sharing data is that researchers do not feel that they have knowledge of how to share data online . This document is an educational aid and resource to provide students with introductory knowledge of how to participate in open science and online data sharing to start their education on these subjects.
Guidelines and Explanations
In light of the changes in psychology, faculty members who teach statistics/methods have reviewed the literature and generated this guide for graduate students. The guide is intended to enhance the quality of student theses by facilitating their engagement in open and transparent research practices and by helping them avoid Questionable Research Practices, many of which are now deemed unethical and covered in the ethics section of textbooks.
This document is an informational tool.
How to Start
In order to follow best practices, some first steps need to be followed. Here is a list of things to do:
- Get an Open Science account. Registration at osf.io is easy!
- If conducting confirmatory hypothesis testing for your thesis, pre-register your hypotheses (see Section 1-Hypothesizing). The Open Science Foundation website has helpful tutorials and guides to get you going.
- Also, pre-register your data analysis plan. Pre-registration typically includes how and when you will stop collecting data, how you will deal with violations of statistical assumptions and points of influence (“outliers”), the specific measures you will use, and the analyses you will use to test each hypothesis, possibly including the analysis script. Again, there is a lot of help available for this.
Exploratory and Confirmatory Research Are Both of Value, But Do Not Confuse the Two
We note that this document largely concerns confirmatory research (i.e., testing hypotheses). We by no means intend to devalue exploratory research. Indeed, it is one of the primary ways that hypotheses are generated for (possible) confirmation. Instead, we emphasize that it is important that you clearly indicate what of your research is exploratory and what is confirmatory. Be clear in your writing and in your preregistration plan. You should explicitly indicate which of your analyses are exploratory and which are confirmatory. Please note also that if you are engaged in exploratory research, then Null Hypothesis Significance Testing (NHST) should probably be avoided (see rationale in Gigerenzer (2004) and Wagenmakers et al., (2012) ).
This document is structured around the stages of thesis work: hypothesizing, design, data collection, analyses, and reporting – consistent with the headings used by Wicherts et al. (2016). We also list the Questionable Research Practices associated with each stage and provide suggestions for avoiding them. We strongly advise going through all of these sections during thesis/dissertation proposal meetings because a priori decisions need to be made prior to data collection (including analysis decisions).
To help to ensure that the student has informed the committee about key decisions at each stage, there are check boxes at the end of each section.
How to Use This Document in a Proposal Meeting
- Print off a copy of this document and take it to the proposal meeting.
- During the meeting, use the document to seek assistance from faculty to address potential problems.
- Revisit responses to issues raised by this document (especially the Analysis and Reporting Stages) when you are seeking approval to proceed to defense.
Consultation and Help Line
Note that the Center for Open Science now has a help line (for individual researchers and labs) you can call for help with open science issues. They also have training workshops. Please see their website for details.
- Hypothesizing
- Data Collection
- Printer-friendly version
- PDF version
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Mathematics PhD theses
A selection of Mathematics PhD thesis titles is listed below, some of which are available online:
2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991
Reham Alahmadi - Asymptotic Study of Toeplitz Determinants with Fisher-Hartwig Symbols and Their Double-Scaling Limits
Anne Sophie Rojahn – Localised adaptive Particle Filters for large scale operational NWP model
Melanie Kobras – Low order models of storm track variability
Ed Clark – Vectorial Variational Problems in L∞ and Applications to Data Assimilation
Katerina Christou – Modelling PDEs in Population Dynamics using Fixed and Moving Meshes
Chiara Cecilia Maiocchi – Unstable Periodic Orbits: a language to interpret the complexity of chaotic systems
Samuel R Harrison – Stalactite Inspired Thin Film Flow
Elena Saggioro – Causal network approaches for the study of sub-seasonal to seasonal variability and predictability
Cathie A Wells – Reformulating aircraft routing algorithms to reduce fuel burn and thus CO 2 emissions
Jennifer E. Israelsson – The spatial statistical distribution for multiple rainfall intensities over Ghana
Giulia Carigi – Ergodic properties and response theory for a stochastic two-layer model of geophysical fluid dynamics
André Macedo – Local-global principles for norms
Tsz Yan Leung – Weather Predictability: Some Theoretical Considerations
Jehan Alswaihli – Iteration of Inverse Problems and Data Assimilation Techniques for Neural Field Equations
Jemima M Tabeart – On the treatment of correlated observation errors in data assimilation
Chris Davies – Computer Simulation Studies of Dynamics and Self-Assembly Behaviour of Charged Polymer Systems
Birzhan Ayanbayev – Some Problems in Vectorial Calculus of Variations in L∞
Penpark Sirimark – Mathematical Modelling of Liquid Transport in Porous Materials at Low Levels of Saturation
Adam Barker – Path Properties of Levy Processes
Hasen Mekki Öztürk – Spectra of Indefinite Linear Operator Pencils
Carlo Cafaro – Information gain that convective-scale models bring to probabilistic weather forecasts
Nicola Thorn – The boundedness and spectral properties of multiplicative Toeplitz operators
James Jackaman – Finite element methods as geometric structure preserving algorithms
Changqiong Wang - Applications of Monte Carlo Methods in Studying Polymer Dynamics
Jack Kirk - The molecular dynamics and rheology of polymer melts near the flat surface
Hussien Ali Hussien Abugirda - Linear and Nonlinear Non-Divergence Elliptic Systems of Partial Differential Equations
Andrew Gibbs - Numerical methods for high frequency scattering by multiple obstacles (PDF-2.63MB)
Mohammad Al Azah - Fast Evaluation of Special Functions by the Modified Trapezium Rule (PDF-913KB)
Katarzyna (Kasia) Kozlowska - Riemann-Hilbert Problems and their applications in mathematical physics (PDF-1.16MB)
Anna Watkins - A Moving Mesh Finite Element Method and its Application to Population Dynamics (PDF-2.46MB)
Niall Arthurs - An Investigation of Conservative Moving-Mesh Methods for Conservation Laws (PDF-1.1MB)
Samuel Groth - Numerical and asymptotic methods for scattering by penetrable obstacles (PDF-6.29MB)
Katherine E. Howes - Accounting for Model Error in Four-Dimensional Variational Data Assimilation (PDF-2.69MB)
Jian Zhu - Multiscale Computer Simulation Studies of Entangled Branched Polymers (PDF-1.69MB)
Tommy Liu - Stochastic Resonance for a Model with Two Pathways (PDF-11.4MB)
Matthew Paul Edgington - Mathematical modelling of bacterial chemotaxis signalling pathways (PDF-9.04MB)
Anne Reinarz - Sparse space-time boundary element methods for the heat equation (PDF-1.39MB)
Adam El-Said - Conditioning of the Weak-Constraint Variational Data Assimilation Problem for Numerical Weather Prediction (PDF-2.64MB)
Nicholas Bird - A Moving-Mesh Method for High Order Nonlinear Diffusion (PDF-1.30MB)
Charlotta Jasmine Howarth - New generation finite element methods for forward seismic modelling (PDF-5,52MB)
Aldo Rota - From the classical moment problem to the realizability problem on basic semi-algebraic sets of generalized functions (PDF-1.0MB)
Sarah Lianne Cole - Truncation Error Estimates for Mesh Refinement in Lagrangian Hydrocodes (PDF-2.84MB)
Alexander J. F. Moodey - Instability and Regularization for Data Assimilation (PDF-1.32MB)
Dale Partridge - Numerical Modelling of Glaciers: Moving Meshes and Data Assimilation (PDF-3.19MB)
Joanne A. Waller - Using Observations at Different Spatial Scales in Data Assimilation for Environmental Prediction (PDF-6.75MB)
Faez Ali AL-Maamori - Theory and Examples of Generalised Prime Systems (PDF-503KB)
Mark Parsons - Mathematical Modelling of Evolving Networks
Natalie L.H. Lowery - Classification methods for an ill-posed reconstruction with an application to fuel cell monitoring
David Gilbert - Analysis of large-scale atmospheric flows
Peter Spence - Free and Moving Boundary Problems in Ion Beam Dynamics (PDF-5MB)
Timothy S. Palmer - Modelling a single polymer entanglement (PDF-5.02MB)
Mohamad Shukor Talib - Dynamics of Entangled Polymer Chain in a Grid of Obstacles (PDF-2.49MB)
Cassandra A.J. Moran - Wave scattering by harbours and offshore structures
Ashley Twigger - Boundary element methods for high frequency scattering
David A. Smith - Spectral theory of ordinary and partial linear differential operators on finite intervals (PDF-1.05MB)
Stephen A. Haben - Conditioning and Preconditioning of the Minimisation Problem in Variational Data Assimilation (PDF-3.51MB)
Jing Cao - Molecular dynamics study of polymer melts (PDF-3.98MB)
Bonhi Bhattacharya - Mathematical Modelling of Low Density Lipoprotein Metabolism. Intracellular Cholesterol Regulation (PDF-4.06MB)
Tamsin E. Lee - Modelling time-dependent partial differential equations using a moving mesh approach based on conservation (PDF-2.17MB)
Polly J. Smith - Joint state and parameter estimation using data assimilation with application to morphodynamic modelling (PDF-3Mb)
Corinna Burkard - Three-dimensional Scattering Problems with applications to Optical Security Devices (PDF-1.85Mb)
Laura M. Stewart - Correlated observation errors in data assimilation (PDF-4.07MB)
R.D. Giddings - Mesh Movement via Optimal Transportation (PDF-29.1MbB)
G.M. Baxter - 4D-Var for high resolution, nested models with a range of scales (PDF-1.06MB)
C. Spencer - A generalization of Talbot's theorem about King Arthur and his Knights of the Round Table.
P. Jelfs - A C-property satisfying RKDG Scheme with Application to the Morphodynamic Equations (PDF-11.7MB)
L. Bennetts - Wave scattering by ice sheets of varying thickness
M. Preston - Boundary Integral Equations method for 3-D water waves
J. Percival - Displacement Assimilation for Ocean Models (PDF - 7.70MB)
D. Katz - The Application of PV-based Control Variable Transformations in Variational Data Assimilation (PDF- 1.75MB)
S. Pimentel - Estimation of the Diurnal Variability of sea surface temperatures using numerical modelling and the assimilation of satellite observations (PDF-5.9MB)
J.M. Morrell - A cell by cell anisotropic adaptive mesh Arbitrary Lagrangian Eulerian method for the numerical solution of the Euler equations (PDF-7.7MB)
L. Watkinson - Four dimensional variational data assimilation for Hamiltonian problems
M. Hunt - Unique extension of atomic functionals of JB*-Triples
D. Chilton - An alternative approach to the analysis of two-point boundary value problems for linear evolutionary PDEs and applications
T.H.A. Frame - Methods of targeting observations for the improvement of weather forecast skill
C. Hughes - On the topographical scattering and near-trapping of water waves
B.V. Wells - A moving mesh finite element method for the numerical solution of partial differential equations and systems
D.A. Bailey - A ghost fluid, finite volume continuous rezone/remap Eulerian method for time-dependent compressible Euler flows
M. Henderson - Extending the edge-colouring of graphs
K. Allen - The propagation of large scale sediment structures in closed channels
D. Cariolaro - The 1-Factorization problem and same related conjectures
A.C.P. Steptoe - Extreme functionals and Stone-Weierstrass theory of inner ideals in JB*-Triples
D.E. Brown - Preconditioners for inhomogeneous anisotropic problems with spherical geometry in ocean modelling
S.J. Fletcher - High Order Balance Conditions using Hamiltonian Dynamics for Numerical Weather Prediction
C. Johnson - Information Content of Observations in Variational Data Assimilation
M.A. Wakefield - Bounds on Quantities of Physical Interest
M. Johnson - Some problems on graphs and designs
A.C. Lemos - Numerical Methods for Singular Differential Equations Arising from Steady Flows in Channels and Ducts
R.K. Lashley - Automatic Generation of Accurate Advection Schemes on Structured Grids and their Application to Meteorological Problems
J.V. Morgan - Numerical Methods for Macroscopic Traffic Models
M.A. Wlasak - The Examination of Balanced and Unbalanced Flow using Potential Vorticity in Atmospheric Modelling
M. Martin - Data Assimilation in Ocean circulation models with systematic errors
K.W. Blake - Moving Mesh Methods for Non-Linear Parabolic Partial Differential Equations
J. Hudson - Numerical Techniques for Morphodynamic Modelling
A.S. Lawless - Development of linear models for data assimilation in numerical weather prediction .
C.J.Smith - The semi lagrangian method in atmospheric modelling
T.C. Johnson - Implicit Numerical Schemes for Transcritical Shallow Water Flow
M.J. Hoyle - Some Approximations to Water Wave Motion over Topography.
P. Samuels - An Account of Research into an Area of Analytical Fluid Mechnaics. Volume II. Some mathematical Proofs of Property u of the Weak End of Shocks.
M.J. Martin - Data Assimulation in Ocean Circulation with Systematic Errors
P. Sims - Interface Tracking using Lagrangian Eulerian Methods.
P. Macabe - The Mathematical Analysis of a Class of Singular Reaction-Diffusion Systems.
B. Sheppard - On Generalisations of the Stone-Weisstrass Theorem to Jordan Structures.
S. Leary - Least Squares Methods with Adjustable Nodes for Steady Hyperbolic PDEs.
I. Sciriha - On Some Aspects of Graph Spectra.
P.A. Burton - Convergence of flux limiter schemes for hyperbolic conservation laws with source terms.
J.F. Goodwin - Developing a practical approach to water wave scattering problems.
N.R.T. Biggs - Integral equation embedding methods in wave-diffraction methods.
L.P. Gibson - Bifurcation analysis of eigenstructure assignment control in a simple nonlinear aircraft model.
A.K. Griffith - Data assimilation for numerical weather prediction using control theory. .
J. Bryans - Denotational semantic models for real-time LOTOS.
I. MacDonald - Analysis and computation of steady open channel flow .
A. Morton - Higher order Godunov IMPES compositional modelling of oil reservoirs.
S.M. Allen - Extended edge-colourings of graphs.
M.E. Hubbard - Multidimensional upwinding and grid adaptation for conservation laws.
C.J. Chikunji - On the classification of finite rings.
S.J.G. Bell - Numerical techniques for smooth transformation and regularisation of time-varying linear descriptor systems.
D.J. Staziker - Water wave scattering by undulating bed topography .
K.J. Neylon - Non-symmetric methods in the modelling of contaminant transport in porous media. .
D.M. Littleboy - Numerical techniques for eigenstructure assignment by output feedback in aircraft applications .
M.P. Dainton - Numerical methods for the solution of systems of uncertain differential equations with application in numerical modelling of oil recovery from underground reservoirs .
M.H. Mawson - The shallow-water semi-geostrophic equations on the sphere. .
S.M. Stringer - The use of robust observers in the simulation of gas supply networks .
S.L. Wakelin - Variational principles and the finite element method for channel flows. .
E.M. Dicks - Higher order Godunov black-oil simulations for compressible flow in porous media .
C.P. Reeves - Moving finite elements and overturning solutions .
A.J. Malcolm - Data dependent triangular grid generation. .
Princeton University Library
Phd dissertation and master's thesis submission guidelines.
The Princeton University Archives at the Mudd Manuscript Library is the repository for Ph.D. dissertations and Master’s theses. The Princeton University Archives partners with ProQuest to publish and distribute Princeton University dissertations beyond the campus community.
Below you will find instructions on the submission process and the formatting requirements for your Ph.D. dissertation or Master's thesis. If you have questions about this process, please use our Ask Us form or visit the Mudd Manuscript Library during our open hours.
Ph.D Dissertation Submission Process
The first step is for the student to prepare their dissertation according to the Dissertation Formatting Requirements . Near the time of the final public oral examination (FPO) (shortly before or immediately after) the student must complete the online submission of their dissertation via the ProQuest UMI ETD Administrator website . Students are required to upload a PDF of their dissertation, choose publishing options, enter subject categories and keywords, and make payment to ProQuest (if fees apply). This step will take roughly 20-25 minutes.
After the FPO the student should log on to TigerHub and complete the checkout process. When this step is complete, Mudd Library will be notified for processing. This step will occur M-F during business hours. The Mudd Library staff member will review, apply the embargo (when applicable), and approve the dissertation submission in ProQuest. You will receive an email notification of the approval from ProQuest when it has been approved or needs revisions.
The vast majority of students will not be required to submit a bound copy of their dissertation to the library. Only students who have removed content from the PDF to avoid copyright infringement are required to submit a bound copy to the library. This unredacted, bound version of the dissertation must be formatted according to the Dissertation Formatting Requirements , and delivered by hand, mail, or delivery service to the Mudd Manuscript Library by the degree date deadline in order to be placed on the degree list. Address the bound copy to: Attn: Dissertations, Mudd Manuscript Library, 65 Olden Street, Princeton, NJ 08540.
ProQuest Publishing Options
When you submit your dissertation to the ProQuest ETD Administrator site, you will be given two options: Traditional Publishing or Open Access Publishing Plus. ProQuest compares the two options in their Open Access Overview document . Full details will be presented in the ProQuest ETD Administrator site.
Traditional Publishing
No fee is paid to ProQuest; your dissertation will be available in full text to subscribing institutions only through the ProQuest Dissertations & Theses Global ; If you have an embargo, your dissertation will be unavailable for viewing or purchase through the subscription database during the embargo period.
Open Access Publishing Plus
$95 fee to ProQuest; your dissertation will be available in full text through the Internet to anyone via the ProQuest Database ; if you have an embargo, your dissertation will be unavailable for viewing through the open access database during the embargo period.
Optional Service: Copyright Registration
$75 fee to ProQuest; ProQuest offers the optional service of registering your copyright on your behalf. The dissertation author owns the copyright to their dissertation regardless of copyright registration. Registering your copyright makes a public record of your copyright claim and may entitle you to additional compensation should your copyright be infringed upon. For a full discussion of your dissertation and copyright, see ProQuest’s Copyright and Your Dissertation .
If you have questions regarding the ProQuest publishing options, contact their Author and School Relations team at 1-800-521-0600 ext. 77020 or via email at [email protected] .
Princeton’s Institutional Repository, DataSpace
Each Princeton University dissertation is deposited in Princeton’s Institutional Repository, DataSpace . Dissertations will be freely available on the Internet except during an embargo period. If your dissertation is embargoed, the PDF will be completely restricted during the embargo period. The bound copy, however, will be available for viewing in the Mudd Manuscript Library reading room during the embargo.
According to the Graduate School’s embargo policy , students can request up to a two-year embargo on their dissertation, with the potential for renewal by petition. If approved, the embargo would apply to the dissertation in ProQuest, as well as in Princeton’s digital repository, DataSpace . Students in the sciences and engineering seeking patents or pursuing journal articles may be approved for a shorter embargo period. Students must apply for the embargo during the Advanced Degree Application process . More information can be found on the Graduate School's Ph.D. Publication, Access and Embargoing webpage .
Those who have been approved for the embargo can choose "Traditional Publishing" or "Open Access Plus" publishing when they complete their online submission to ProQuest. Mudd Manuscript Library staff will apply the embargo in the ProQuest ETD system at the time of submission of materials to the Library. In the case of Open Access Plus, the dissertation would become freely available on the ProQuest open access site when the embargo expires. The embargo in ProQuest will also apply to the embargo in Princeton’s digital repository, DataSpace
Those who wish to request a renewal of an existing embargo must email Assistant Dean Geoffrey Hill and provide the reason for the extension. An embargo renewal must be requested in writing at least one month before the original embargo has expired, but may not be requested more than three months prior to the embargo expiration date. Embargoes cannot be reinstituted after having expired. Embargoes are set to expire two years from the date on which the Ph.D. was awarded (degrees are awarded five times per year at Board of Trustee meetings); this date will coincide with the degree date (month and year) on the title page of your dissertation. Please note: You, the student, are responsible for keeping track of the embargo period--notifications will not be sent.
- To find the exact date of an embargo expiration, individuals can find their dissertation in DataSpace , and view the box at the bottom of the record, which will indicate the embargo expiration date.
- The Graduate School will inform the Mudd Library of all renewals and Mudd Library staff will institute the extensions in ProQuest and DataSpace .
- Princeton University Archives' Dissertation Formatting Requirements (PDF download) document provides detailed information on how to prepare the dissertation PDF and bound volume (if you are required to submit a bound volume). Please take special note of how to format the title page (a title page example is downloadable from the upper-right-hand side of this webpage). The title page must list your adviser’s name.
- ProQuest's Preparing Your Manuscript guide offers additional information on formatting the PDF. Where there are discrepancies with the Princeton University Archives Dissertation Formatting Requirements document, the Princeton University Archives requirements should be followed. Special consideration should be paid to embedding fonts in the PDF.
- ProQuest ETD Administrator Resources and Guidelines web page offers several guides to assist you in preparing your PDF, choosing publishing options, learning about copyright considerations, and more.
- ProQuest's Support and Training Department can assist with issues related to creating and uploading PDFs and any questions regarding technical issues with the online submission site.
Whether a student pays fees to ProQuest in the ETD Administrator Site depends on the publishing option they choose, and if they opt to register their copyright (if a student selects Traditional Publishing, and does not register their copyright, no charges are incurred). Fees are to be submitted via the UMI ETD Administrator Site. Publishing and copyright registration fees are payable by Visa, MasterCard, or American Express and a small service tax may be added to the total. The options listed below will be fully explained in the ETD Administrator site.
- Traditional without copyright registration: $0 to ProQuest (online)
- Traditional with copyright registration: $75 to ProQuest (online)
- Open Access without copyright registration: $95 to ProQuest (online)
- Open Access ($95) with copyright registration ($55): $150 to ProQuest (online)
Degrees are granted five times per year at Board of Trustee meetings. Deadlines for materials to be submitted to the Mudd Manuscript Library are set by the Office of the Graduate School . The title page of your dissertation must state the month and year of the board meeting at which you will be granted your degree, for example “April 2023.”
Academic Year 2024-2025
- Friday, August 30, 2024, degree date "September 2024"
- Thursday, October 31, 2024, degree date "November 2024"
- Tuesday, December 31, 2024, degree date "January 2025"
- Friday, February 28, 2025, degree date "March 2025"
- Thursday, May 8, 2025, degree date "May 2025"
Please note: If a student is granted an extension for submission of their materials after a deadline has passed, the Mudd Manuscript Library must have written confirmation of the extension from the Office of the Graduate School in the form of an email to [email protected] .
One non-circulating , bound copy of each dissertation produced until and including the January 2022 degree list is held in the collection of the University Archives. For dissertations submitted prior to September 2011, a circulating , bound copy of each dissertation may also be available. Information about these dissertations can be found in Princeton University Library's catalog .
Electronic Copy (PDF) in ProQuest
ProQuest Dissertation Publishing distributes Princeton University dissertations. Members of the Princeton University community can access most dissertations through the ProQuest Dissertations and Theses subscription database, which is made available through the Princeton University Library. For students that choose "Open Access Plus publishing," their dissertations are available freely on the internet via ProQuest Dissertations and Theses . Dissertations are available for purchase through ProQuest Dissertation Express . Once the dissertation has been accepted by the Mudd Library it will be released to ProQuest following the Board of Trustee meeting on which your degree is conferred. Bound copies ordered from ProQuest will be printed following release. Please note, dissertations under embargo are not available in full text through the ProQuest Dissertations and Theses subscription database or for sale via ProQuest Dissertation Express during the embargo period.
Electronic Copy (PDF) in Princeton's Institutional Repository, DataSpace
Beginning in the fall of 2011, dissertations will be available through the internet in full-text via Princeton's digital repository, DataSpace . (Embargoed dissertations become available to the world once the embargo expires.)
Interlibrary Loan
Dissertations that have bound copies and are not under embargo are available through Interlibrary Loan (ILL) to libraries in the United States and Canada, either through hard copy or PDF. If PDFs are available, they can be sent internationally.
Master's Thesis Submission Process
Students who are enrolled in a thesis-based Master’s degree program must upload a PDF of their thesis to Princeton's ETD Administrator site (ProQuest) just prior to completing the final paperwork for the Graduate School. These programs currently include:
- The Department of Chemical and Biological Engineering (M.S.E.)
- The Department of Civil and Environmental Engineering (M.S.E.)
- The Department of Computer Science (M.S.E.)
- The Department of Electrical and Computer Engineering (M.S.E.)
- The Department of Mechanical and Aerospace Engineering (M.S.E.)
- The Department of Operations Research and Financial Engineering (M.S.E.)
- The Department of Near Eastern Studies (M.A.)
The PDF should be formatted according to our Dissertation Formatting Requirements (PDF download). The Mudd Library will review and approve the submission upon notification from the Graduate School that your final paperwork is ready for this step. Bound copies are no longer required or accepted for Master's theses.
Students who are not in a thesis-based Master's degree program do not need to make a submission to the library upon graduation. If you have questions, please complete the form on the Ask Special Collections page.
- Dissertation Formatting Requirements
Volumetric image analysis: optical flow, registration and segmentation
Thesis type.
- Thesis (PhD)
Thesis note
Copyright statement, supervisors, usage metrics.
IMAGES
COMMENTS
Dissertation TBA. Sponsor: Sumit Mukherjee. 2021 Ph.D. Dissertations. Tong Li. On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods. Sponsor: Liam Paninski. Ding Zhou. Advances in Statistical Machine Learning Methods for Neural Data Science. Sponsor: Liam Paninski.
Alex Luedtke, Lalit Kumar Jain. Statistical Learning and Modeling with Graphs and Networks. Jerry Wei. Yen-Chi Chen, Tyler Mccormick. 2023. Title. Author. Supervisor. Statistical Methods for the Analysis and Prediction of Hierarchical Time Series Data with Applications to Demography.
Theses/Dissertations from 2016 PDF. A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida, Joy Marie D'andrea. PDF. Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize, Sherlene Enriquez-Savery. PDF. Putnam's Inequality and Analytic Content in the Bergman Space, Matthew Fleeman. PDF
PhD candidates: You are welcome and encouraged to deposit your dissertation here, but be aware that 1. it is optional, not required (the ProQuest deposit is required); and 2. it will be available to everyone online; there is no embargo for dissertations in the UNL Digital Commons. Master's candidates: Deposit of your thesis or project is required.
The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by ...
Dissertation Advisor: Marten Wegkamp and Florentina Bunea. 2021. - "Nonparametric and semiparametric approaches to functional data modeling". - "Deep probabilistic models for sequential prediction". - "Off-policy evaluation and learning for interactive systems". - "Scalable and reliable inference for probabilistic modeling".
The PhD degree in statistics is designed for students who wish to pursue a career in statistics research in academia, government, or industry. The curriculum is designed to provide a strong in-depth and broad training in statistical theory, methodology, computation, and applications. Students begin their research experience in the first year ...
The Doctor of Philosophy program in the Field of Statistics is intended to prepare students for a career in research and teaching at the University level or in equivalent positions in industry or government. A PhD degree requires writing and defending a dissertation. Students graduate this program with a broad set of skills, from the ability to interact collaboratively with researchers in ...
Defend the PhD disseration. Dissertation Requirements. The student is required to complete a dissertation in some area of theoretical statistics, applied statistics, or probability that is an original contribution of publishable quality and must successfully defend the dissertation in an oral presentation open to the University community.
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Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests.
The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry. The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas.
Dissertation and Defense. Students are also required to prepare a dissertation, which must present an original contribution to the general area of statistics and/or probability. A Ph.D. degree must include at least 15 credits of GRAD 6950. Doctoral Dissertation Research. Once complete, Ph.D. students must present a defense of their dissertation ...
DStat thesis: Challenges in modelling pharmacogenetic data: Investigating biomarker and clinical response simultaneously for optimal dose prediction. Rungruttikarn Moungmai. Family-based genetic association studies in a likelihood framework. Michael Dunbar. Multiple hydro-ecological stressor interactions assessed using statistical models.
Mathematical Statistics I: STAT 6202: Mathematical Statistics II: STAT 8257: Probability: STAT 8263: Advanced Statistical Theory I: B. An examination to determine the student's readiness to carry out the proposed dissertation research: A dissertation demonstrating the candidate's ability to do original research in one area of probability or ...
PhD Dissertations. Junghee Bae. The commercial activity of nonprofit human service organizations analysis approach: latent class growth analysis approach | Ph. D. | 05/2019. Stephanie Marie Eick. Psychosocial stress among pregnant women in Puerto Rico | Ph. D. | 05/2019.
PhD students in econometrics and statistics apply statistical methods to a wide range of business problems, from the effectiveness of machine-learning tools to video-game preferences. Our graduates go on to work in high-profile institutions, generally in academia, finance, or data science. Current Students.
The following is a list of recent statistics and biostatistics PhD Dissertations and Masters Theses. Jeffrey Gory (2017) PhD Dissertation (Statistics): Marginally Interpretable Generalized Linear Mixed Models Advisors: Peter Craigmile & Steven MacEachern Yi Lu (2017) PhD Dissertation (Statistics): Function Registration from a Bayesian Perspective Advisors: Radu Herbei & Sebastian Kurtek
UT Dallas > Mathematical Sciences > Graduate Programs > PhD Dissertations in Statistics. Year of Graduation. Student. Supervising Professor. Dissertation Title. 2023. Tejasv Bedi. Qiwei Li. BAYESIAN MODEL BASED CLUSTER ANALYSIS AND ITS APPLICATIONS IN EPIDEMIOLOGY & MICROBIOLOGY.
The Ph.D. in Statistics provides students with rigorous training in the theory, methodology, computation, and application of statistics. ... A dissertation. View full degree requirements. Courses. Students entering the program with a bachelor's degree are typically required to take 16 to 18 courses to earn a Ph.D. in Statistics.
Recent MMath Theses Recent PhD theses News & Events News & Events News Events ... Department of Statistics and Actuarial Science (SAS) Mathematics 3 (M3) University of Waterloo Administrative Staff Directory Phone: 519-888-4567, ext. 43550 Fax: 519-746-1875 ...
Guidelines and Explanations. In light of the changes in psychology, faculty members who teach statistics/methods have reviewed the literature and generated this guide for graduate students. The guide is intended to enhance the quality of student theses by facilitating their engagement in open and transparent research practices and by helping ...
A selection of Mathematics PhD thesis titles is listed below, some of which are available online: 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991. 2024. Reham Alahmadi - Asymptotic Study of Toeplitz Determinants with Fisher-Hartwig Symbols and Their Double-Scaling Limits
Within one year of passing QII, the student forms a PhD Candidacy Examination Committee (CEC). The CEC consists of the student's dissertation advisor, who must be a P-status faculty member of the Interdisciplinary PhD Program in Biostatistics, and at least three other graduate faculty members, two of whom must be P-status faculty of the program.
According to the Graduate School's embargo policy, students can request up to a two-year embargo on their dissertation, with the potential for renewal by petition.If approved, the embargo would apply to the dissertation in ProQuest, as well as in Princeton's digital repository, DataSpace.Students in the sciences and engineering seeking patents or pursuing journal articles may be approved ...
This thesis presents more accurate and efficient methods for volumetric image analysis in terms of Optical Flow, Registration and Segmentation. Firstly, a relationship between the estimation accuracy and the required amount of smoothness for motion estimation from a robust statistics perspective is developed. Next, a fast and accurate non-rigid registration method for intra-modality volumetric ...
Applied Health Statistics I: Descriptive, Associative and Comparative Statistics . 4 . ... (PhD) Program of Study for Students Matriculating 2024-25 Academic Year. ... Total credits 27 *A minimum of 27 dissertation credits are required to graduate. This includes a minimum of 24 dissertation credits (603) and a minimum of 3 credits of dissertation
PHD COURSE SEQUENCES 2024-2025 BS ... N299A Nursing Research Ethics 2 N291A Applied Statistics & Analytics for Health Sciences Research I 4 N291B Applied Statistics & Analytics for Health ... their dissertation and twice by students using quantitative methods for their dissertation. • N596 may be taken any quarter, any year, prior to ...
CED 8002 Dissertation Continued as needed after successful proposal defense. (1 credit per semester) 63 Total Credits (Excluding CED 8002. Additional coursework may be necessary to fulfill the foundational requirement in Counseling and Counselor Education, subject to a review of transcripts). * CED 8001 Dissertation - limited to 12 credits ...
On May 8, 2024, Cami Hippee, a graduate student in the Microbiology Graduate Program, effectively defended her PhD thesis entitled "Measles virus exits human airway epithelia via cell detachment pathways." She is pictured with her mentor, Patrick Sinn, PhD. Research Measles virus (MeV) is the most