phd thesis statistics

2024
Title	Author	Supervisor
Estimation and Inference of Optimal Policies		,
Statistical Learning and Modeling with Graphs and Networks		,

2023
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

2022
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

2021
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

2020
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

2019
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

2018
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

2017
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"		,

2016
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"

2015
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"

2014
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"

2013
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"

2012
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"		,

2011
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"

2010
Title	Author	Supervisor
"Convex analysis methods in shape constrained estimation."
"Estimating social contact networks to improve epidemic simulation models"
"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"

2009
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"

2008
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"		,

2007
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"

2006
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"		,

2005
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"		, ,

2004
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"

2003
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"		,

2002
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"		,

2001
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"		,

2000
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"

1999
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"

1998
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"

1997
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"

1996
Title	Author	Supervisor
"Bootstrapping functional m-estimators"
"Variability estimation in linear inverse problems"
"Inference in a discrete parameter space"

1995
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"

1994
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"

1993
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"

1992
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"

1991
Title	Author	Supervisor
"General-weights bootstrap of the empirical process"
"The weighted likelihood bootstrap and an algorithm for prepivoting"

1990
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"

1989
Title	Author	Supervisor
"Estimation of mixing and mixed distributions"
"Classical inference in spatial statistics"

1988
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"

1987
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"

1986
Title	Author	Supervisor
"Estimation for infinite variance autoregressive processes"
"A computer system for Monte Carlo experimentation"

1985
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"

1983
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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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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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);
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 our PhD 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 Graduate Director. Each student should meet at least once a quarter with the Graduate Director 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

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 Ph.D. Candidacy, a university milestone, by the end of 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.

Recent Dissertation Topics

Marty Wells and a student look over papers

Kerstin Emily Frailey - “PRACTICAL DATA QUALITY FOR MODERN DATA & MODERN USES, WITH APPLICATIONS TO AMERICA’S COVID-19 DATA"

Dissertation Advisor: Martin Wells

Initial job placement: Co-Founder & CEO

David Kent - “Smoothness-Penalized Deconvolution: Rates of Convergence, Choice of Tuning Parameter, and Inference"

Dissertation Advisor: David Ruppert

Initial job placement: VISITING ASSISTANT PROFESSOR - Cornell University

Yuchen Xu - “Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation”

Dissertation Advisor: David Matteson

Initial job placement: Postdoctoral Fellow - UCLA

Siyi Deng - “Optimal and Safe Semi-supervised Estimation and Inference for High-dimensional Linear Regression"

Dissertation Advisor: Yang Ning

Initial job placement: Data Scientist - TikTok

Peter (Haoxuan) Wu - “Advances in adaptive and deep Bayesian state-space models”

Initial job placement: Quantitative Researcher - DRW

Grace Deng - “Generative models and Bayesian spillover graphs for dynamic networks”

Initial job placement: Data Scientist - Research at Google

Samriddha Lahiry - “Some problems of asymptotic quantum statistical inference”

Dissertation Advisor: Michael Nussbaum

Initial job placement: Postdoctoral Fellow - Harvard University

Yaosheng Xu - “WWTA load-balancing for parallel-server systems with heterogeneous servers and multi-scale heavy traffic limits for generalized Jackson networks”

Dissertation Advisor: Jim Dai

Initial job placement: Applied Scientist - Amazon

Seth Strimas-Mackey - “Latent structure in linear prediction and corpora comparison”

Dissertation Advisor: Marten Wegkamp and Florentina Bunea

Initial job placement: Data Scientist at Google

Tao Zhang - “Topics in modern regression modeling”

Dissertation Advisor: David Ruppert and Kengo Kato

Initial job placement: Quantitative Researcher - Point72

Wentian Huang - “Nonparametric and semiparametric approaches to functional data modeling”

Initial job placement: Ernst & Young

Binh Tang - “Deep probabilistic models for sequential prediction”

Initial job placement: Amazon

Yi Su - “Off-policy evaluation and learning for interactive systems"

Dissertation Advisor: Thorsten Joachims

Initial job placement: Berkeley (postdoc)

Ruqi Zhang - “Scalable and reliable inference for probabilistic modeling”

Dissertation Advisor: Christopher De Sa

Jason Sun - “Recent developments on Matrix Completion"

Initial job placement: LinkedIn

Indrayudh Ghosal - “Model combinations and the Infinitesimal Jackknife : how to refine models with boosting and quantify uncertainty”

Dissertation Advisor: Giles Hooker

Benjamin Ryan Baer - “Contributions to fairness and transparency”

Initial job placement: Rochester (postdoc)

Megan Lynne Gelsinger - “Spatial and temporal approaches to analyzing big data”

Dissertation Advisor: David Matteson and Joe Guinness

Initial job placement: Institute for Defense Analysis

Zhengze Zhou - “Statistical inference for machine learning : feature importance, uncertainty quantification and interpretation stability”

Initial job placement: Facebook

Huijie Feng - “Estimation and inference of high-dimensional individualized threshold with binary responses”

Initial job placement: Microsoft

Xiaojie Mao - “Machine learning methods for data-driven decision making : contextual optimization, causal inference, and algorithmic fairness”

Dissertation Advisor: Nathan Kallus and Madeleine Udell

Initial job placement: Tsinghua University, China

Xin Bing - “Structured latent factor models : Identifiability, estimation, inference and prediction”

Initial job placement: Cambridge (postdoc), University of Toronto

Yang Liu - “Nonparametric regression and density estimation on a network"

Dissertation Advisor: David Ruppert and Peter Frazier

Initial job placement: Research Analyst - Cubist Systematic Strategies

Skyler Seto - “Learning from less : improving and understanding model selection in penalized machine learning problems”

Initial job placement: Machine Learning Researcher - Apple

Jiekun Feng - “Markov chain, Markov decision process, and deep reinforcement learning with applications to hospital management and real-time ride-hailing”

Initial job placement:

Wenyu Zhang - “Methods for change point detection in sequential data”

Initial job placement: Research Scientist - Institute for Infocomm Research

Liao Zhu - “The adaptive multi-factor model and the financial market"

Initial job placement: Quantitative Researcher - Two Sigma

Xiaoyun Quan - “Latent Gaussian copula model for high dimensional mixed data, and its applications”

Dissertation Advisor: James Booth and Martin Wells

Praphruetpong (Ben) Athiwaratkun - "Density representations for words and hierarchical data"

Dissertation Advisor: Andrew Wilson

Initial job placement: AI Scientist - AWS AI Labs

Yiming Sun - “High dimensional data analysis with dependency and under limited memory”

Dissertation Advisor: Sumanta Basu and Madeleine Udell

Zi Ye - “Functional single index model and jensen effect"

Dissertation Advisor: Giles Hooker

Initial job placement: Data & Applied Scientist - Microsoft

Hui Fen (Sarah) Tan - “Interpretable approaches to opening up black-box models”

Dissertation Advisor: Giles Hooker and Martin Wells

Daniel E. Gilbert - “Luck, fairness and Bayesian tensor completion”

Yichen zhou - “asymptotics and interpretability of decision trees and decision tree ensemblesg”.

Initial job placement: Data Scientist - Google

Ze Jin - “Measuring statistical dependence and its applications in machine learning”

Initial job placement: Research Scientist, Facebook Integrity Ranking & ML - Facebook

Xiaohan Yan - “Statistical learning for structural patterns with trees”

Dissertation Advisor: Jacob Bien

Initial job placement: Senior Data Scientist - Microsoft

Guo Yu - “High-dimensional structured regression using convex optimization”

Dan kowal - "bayesian methods for functional and time series data".

Dissertation Advisor: David Matteson and David Ruppert

Initial job placement: assistant professor, Department of Statistics, Rice University

Keegan Kang - "Data Dependent Random Projections"

David sinclair - "model selection results for high dimensional graphical models on binary and count data with applications to fmri and genomics", liu, yanning – "statistical issues in the design and analysis of clinical trials".

Dissertation Advisor: Bruce Turnbull

Nicholson, William Bertil – "Tools for Modeling Sparse Vector Autoregressions"

Tupper, laura lindley – "topics in classification and clustering of high-dimensional data", chetelat, didier – "high-dimensional inference by unbiased risk estimation".

Initial Job Placement: Assistant Professor Universite de Montreal, Montreal, Canada

Gaynanova, Irina – "Estimation Of Sparse Low-Dimensional Linear Projections"

Dissertation Advisor: James Booth

Initial Job Placement: Assistant Professor, Texas A&M, College Station, TX

Mentch, Lucas – "Ensemble Trees and CLTS: Statistical Inference in Machine Learning"

Initial Job Placement: Assistant Professor, University of Pittsburgh, Pittsburgh, PA

Risk, Ben – "Topics in Independent Component Analysis, Likelihood Component Analysis, and Spatiotemporal Mixed Modeling"

Dissertation Advisors: David Matteson and David Ruppert

Initial Job Placement: Postdoctoral Fellow, University of North Carolina, Chapel Hill, NC

Zhao, Yue – "Contributions to the Statistical Inference for the Semiparametric Elliptical Copula Model"

Disseration Advisor: Marten Wegkamp

Initial Job Placement: Postoctoral Fellow, McGill University, Montreal, Canada

Chen, Maximillian Gene – "Dimension Reduction and Inferential Procedures for Images"

Dissertation Advisor: Martin Wells

Earls, Cecelia – Bayesian hierarchical Gaussian process models for functional data analysis

Dissertation Advisor: Giles Hooker

Initial Job Placement: Lecturer, Cornell University, Ithaca, NY

Li, James Yi-Wei – "Tensor (Multidimensional Array) Decomposition, Regression, and Software for Statistics and Machine Learning"

Initial Job Placement: Research Scientist, Yahoo Labs

Schneider, Matthew John – "Three Papers on Time Series Forecasting and Data Privacy"

Dissertation Advisor: John Abowd

Initial Job Placement: Assistant Professor, Northwestern University, Evanston, IL

Thorbergsson, Leifur – "Experimental design for partially observed Markov decision processes"

Initial Job Placement: Data Scientist, Memorial Sloan Kettering Cancer Center, New York, NY

Wan, Muting – "Model-Based Classification with Applications to High-Dimensional Data in Bioinformatics"

Initial Job Placement: Senior Associate, 1010 Data, New York, NY

Johnson, Lynn Marie – "Topics in Linear Models: Methods for Clustered, Censored Data and Two-Stage Sampling Designs"

Dissertation Advisor: Robert Strawderman

Initial Job Placement: Statistical Consultant, Cornell, Statistical Consulting Unit, Ithaca, NY

Tecuapetla Gomez, Inder Rafael – "Asymptotic Inference for Locally Stationary Processes"

Initial Job Placement: Postdoctoral Fellow, Georg-August-Universitat Gottigen, Gottigen, Germany.

Bar, Haim – "Parallel Testing, and Variable Selection -- a Mixture-Model Approach with Applications in Biostatistics"

Dissertation Advisor: James Booth

Initial Job Placement: Postdoc, Department of Medicine, Weill Medical Center, New York, NY

Cunningham, Caitlin – "Markov Methods for Identifying ChIP-seq Peaks"

Initial Job Placement: Assistant Professor, Le Moyne College, Syracuse, NY

Ji, Pengsheng – "Selected Topics in Nonparametric Testing and Variable Selection for High Dimensional Data"

Dissertation Advisor: Michael Nussbaum

Initial Job Placement: Assistant Professor, University of Georgia, Athens, GA

Morris, Darcy Steeg – "Methods for Multivariate Longitudinal Count and Duration Models with Applications in Economics"

Dissertation Advisor: Francesca Molinari

Initial Job Placement: Research Mathematical Statistician, Center for Statistical Research and Methodology, U.S. Census Bureau, Washington DC

Narayanan, Rajendran – "Shrinkage Estimation for Penalised Regression, Loss Estimation and Topics on Largest Eigenvalue Distributions"

Initial Job Placement: Visiting Scientist, Indian Statistical Institute, Kolkata, India

Xiao, Luo – "Topics in Bivariate Spline Smoothing"

Dissertation Advisor: David Ruppert

Initial Job Placement: Postdoc, Johns Hopkins University, Baltimore, MD

Zeber, David – "Extremal Properties of Markov Chains and the Conditional Extreme Value Model"

Dissertation Advisor: Sidney Resnick

Initial Job Placement: Data Analyst, Mozilla, San Francisco, CA

Clement, David – "Estimating equation methods for longitudinal and survival data"

Dissertation Advisor: Robert Strawderman

Initial Job Placement: Quantitative Analyst, Smartodds, London UK

Eilertson, Kirsten – "Estimation and inference of random effect models with applications to population genetics and proteomics"

Dissertation Advisor: Carlos Bustamante

Initial Job Placement: Biostatistician, The J. David Gladstone Institutes, San Francisco CA

Grabchak, Michael – "Tempered stable distributions: properties and extensions"

Dissertation Advisor: Gennady Samorodnitsky

Initial Job Placement: Assistant Professor, UNC Charlotte, Charlotte NC

Li, Yingxing – "Aspects of penalized splines"

Initial Job Placement: Assistant Professor, The Wang Yanan Institute for Studies in Economics, Xiamen University

Lopez Oliveros, Luis – "Modeling end-user behavior in data networks"

Dissertation Advisor: Sidney Resnick

Initial Job Placement: Consultant, Murex North America, New York NY

Ma, Xin – "Statistical Methods for Genome Variant Calling and Population Genetic Inference from Next-Generation Sequencing Data"

Initial Job Placement: Postdoc, Stanford University, Stanford CA

Kormaksson, Matthias – "Dynamic path analysis and model based clustering of microarray data"

Dissertation Advisor: James Booth

Initial Job Placement: Postdoc, Department of Public Health, Weill Cornell Medical College, New York NY

Schifano, Elizabeth – "Topics in penalized estimation"

Initial Job Placement: Postdoc, Department of Biostatistics, Harvard University, Boston MA

Hanlon, Bret – "High-dimensional data analysis"

Dissertation Advisor: Anand Vidyashankar

Shaby, Benjamin – "Tools for hard bayesian computations"

Initial Job Placement: Postdoc, SAMSI, Durham NC

Zipunnikov, Vadim – "Topics on generalized linear mixed models"

Initial Job Placement: Postdoc, Department of Biostatistics, Johns Hopkins University, Baltimore MD

Barger, Kathryn Jo-Anne – "Objective bayesian estimation for the number of classes in a population using Jeffreys and reference priors"

Dissertation Advisor: John Bunge

Initial Job Placement: Pfizer Incorporated

Chan, Serena Suewei – "Robust and efficient inference for linear mixed models using skew-normal distributions"

Initial Job Placement: Statistician, Takeda Pharmaceuticles, Deerfield IL

Lin, Haizhi – "Distressed debt prices and recovery rate estimation"

Dissertation Advisor: Martin Wells

Initial Job Placement: Associate, Fixed Income Department, Credit Suisse Securities (USA), New York, NY

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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

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Statistics PhD theses

2015 onwards.

Abdulrafiu Babatunde Odunuga
Philip Maybank
Natalie Dimier
Chintu Desai	Statistical study designs for phase III pharmacogenetic clinical trials
Frank Owusu-Ansah	Methodology for joint modelling of spatial variation and competition effects in the analysis of varietal selection trials

Supada Charoensawat	A likelihood approach based upon the proportional hazards model for SROC modelling in meta-analysis of diagnostic studies
Pianpool Kirdwichai	A nonparametric regression approach to the analysis of genomewide association studies
Reynaldo Martina	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
Osama Abdulhey	Alcohol consumption and mortality from all and specific causes: the J-hypothesis. A systematic review and meta-analysis of current and historical evidence
Rattana Lerdsuwansri	Generalisation of the Lincoln-Peterson approach to non-binary source variables
Krisana Lanumteang	Estimation of the size of a target population using Capture-Recapture methods based upon multiple sources and continuous time experiments
Rainer-Georg Göldner	Investigation of new single locus and multivariate methods for the analysis of genetic association studies
Isak Neema	Survey and monitoring crimes in Namibia through the likelihood based cluster analysis
Mercedes Andrade Bejarano	Monthly average temperature modelling for Valle del Cauca (Colombia)
Robert Mastrodomenico	Statistical analysis of genetic association studies
Ruth Butler	DStat thesis: An exploration of the statistical consequences of sub-sampling for species identification
Carmen Ybarra Moncada	Multivariate methods with application to spectroscopy
Alun Bedding	The Bayesian analysis of dose titration to effect in Phase II clinical trials in order to design Phase III
Timothy Montague	Adaptive designs for bioequivalence trials
Magnus Kjaer	Clinical trials of cytostatic agents with repeated measurements: using the regression coefficients as response
Kamziah Abd Kudus	Survival analysis models for interval censored data with application to an plantation spacing trial
Isobel Barnes	Point estimation after a sequential clinical trial
Ben Carter	Statistical methodology for the analysis of microarray data
Joanna Burke	Regularised regression in QTL mapping
Alexandre M F G da Silva	Methods for the analysis of multivariate lifetime data with frailty
Harsukhjit Deo	Analysis of a Quantitative Trait Locus for twin data using univariate and multivariate linear mixed effects models
Kim Bolland	The design and analysis of neurological trials yielding repeated ordinal data
Fazil Baksh	Sequential tests of association with applications in genetic epidemiology
Martyn Byng	A statistical model for locating regulatory regions in novel DNA sequences
Rob Deardon	Representation bias in field trials for airborne plant pathogens
Marian Hamshere	Statistical aspects of objects generated by dynamic processes at sea, detected by remote sensing techniques
Mike Branson	The analysis of survival data in which patients switch treatments
Christoph Lang	Generalised estimating equation methods in statistical genetics
V R P Putcha	Random effects in survival analysis

Robin Fletcher	Statistical inversion of surface parameters from ATSR-2 satellite observations
Seth Ohemeng-Dapaah	Methods for analysis and interpretation of genotype by environment interaction
Emmanuelle Vincent	Sequential designs for clinical trials involving multiple treatments
Pi Wen Tsai	Three-level designs robust to model uncertainty
Jo Farebrother	Statistical design and analysis of factorial combination drug trials
Mark Lennon	Design and analysis of multiple site large plot field experiments
Norberto Lavorenti	Fitting models in a bivariate analysis of intercrops
Bernard North	Contributions to survival analysis
Karen Ayres	Measuring genetic correlations within and between loci, with implications for disequilibrium mapping and forensic identification
Andrew Morris	Transmission tests of linkage and association using samples of nuclear families with at least one affected child
Julian Higgins	Exploiting information in random effects meta-analysis
Mohammed Inayat Khan	Improving precision of agricultural field experiments in Pakistan
Luzia Trinca	Blocking response surface designs
Phil Bowtell	Non-linear functional relationships
Louise Burt	Statistical modelling of volcanic hazards
Helen Millns	The application of statistical methods to the analysis of diet and coronary heart disease in Scotland
Dominic Neary	Methods of analysis for ordinal repeated measures data
Graham Pursey	Shape location and classification with reference to fungal spores
Nigel Stallard	Increasing efficiency in the design and analysis of animal toxicology studies
Katarzyna Stepniewska	Some variable selection problems in medical research

Thesis Defense

Smd statistics thesis guide supplement, purpose of this document.

This document provides a guide for the structure and content of a Statistics PhD thesis document. Because thesis topics and methods vary greatly, the requirements for any given thesis may vary from the guidelines presented here as is required to facilitate coherent presentation. However, notwithstanding such exceptions, the structure and content provided below is the standard for a Statistics PhD thesis at the University of Rochester.

This document is meant to be a supplement to the general guidelines of the University of Rochester for preparation of a thesis (“Preparing Your Thesis: a Manual for Graduate Students”), which can be found at the website http://www.rochester.edu/Theses/ThesesManual.pdf , and which governs all theses at this university. This document does not supersede the general guidelines.

Overview of thesis contents

A thesis is a description and interpretation of the research conducted by the candidate that qualifies him/her for the degree of PhD.

Wherever possible (particularly the introductory and final chapters), the thesis should be written so that the material is accessible to those not working in the specialized area of research. Every member of the thesis examination committee should be able to understand the main ideas in the document as a whole, and the details of each section must be understandable to at least one committee member with the expertise to verify that its content is sound.

The document should be written in English with correct spelling and grammar. It is not the job of the committee to proof-read the text. Having the text of the thesis corrected and edited for clarity by a second person is acceptable and highly recommended. A committee member can refuse to accept a thesis with excessive grammatical or typographical errors.

There is no formal minimum or maximum length. The thesis must give an in-depth account of the background and the research question addressed, as well as a detailed description of the methods and results that is typically more specific than that found in the published literature.

Organization of the thesis

The manual titled “Preparing Your Thesis: a Manual for Graduate Students” outlines the overall structure of the thesis in terms of general formatting and required parts such as the Title Page, Abstract, etc. This manual should be consulted for specifications regarding these components. This manual, however, does not address the substantive chapters of the thesis. That guidance is provided herein.

A PhD thesis in Statistics is expected to involve the development of novel statistical methodology and/or provide important contributions to the theory of statistics. It should consist of original work of publishable quality that addresses a unified theme, as opposed to a collection of unrelated methodological developments. A Statistics PhD thesis will typically contain five chapters (although this may vary):

Chapter 1. Introduction

This chapter introduces the research problem and outlines the relevant background. While expansive details of all relevant published works should be avoided, this chapter should summarize all pertinent scientific literature to provide the information necessary for understanding what is currently known and how the thesis will contribute in an important way to expanding this knowledge. This chapter provides the requisite arguments to establish the importance of the problem as a statistical research topic. Often, example data from actual scientific studies are highly useful for motivating the research problem. The chapter should conclude by briefly summarizing the research approach to the thesis and the organization of the remaining chapters.

Chapters 2-4. Distinct Aspects of the Research

Each of these chapters typically addresses a distinct sub-problem related to the general theme of the dissertation. The mathematical development of the novel methodology should be presented in detail. New theorems and proofs (as well as relevant existing theorems) should be provided as necessary for analytical evaluation of the properties of the new methods. Simulation studies may be necessary to empirically evaluate the properties of these methods; the simulation designs should be described in sufficient detail to allow replication of the results by others. Comparisons should be made to existing methods, if any, for addressing the same research problem. Results of the evaluations should be clearly and thoroughly presented in figures and tables that are self-contained.

Example data from actual scientific studies should be used whenever possible (and applicable) to illustrate the utility of the new methodology.

Chapter 5. Conclusions and Future Work

The final chapter should discuss the research findings in a unified framework and provide an overall perspective for the reader, including limitations of the research and future work to be performed. It may be helpful to briefly recapitulate the state of the field at the outset of the research, summarize the main results of the thesis, explain how the current work provides an important contribution to existing knowledge, point out any limitations of the newly-developed methods, raise new questions that may have arisen out of the research, and propose future work to address existing gaps in knowledge.

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What do senior theses in Statistics look like?

This is a brief overview of thesis writing; for more information, please see our website here . Senior theses in Statistics cover a wide range of topics, across the spectrum from applied to theoretical. Typically, senior theses are expected to have one of the following three flavors:

1. Novel statistical theory or methodology, supported by extensive mathematical and/or simulation results, along with a clear account of how the research extends or relates to previous related work.

2. An analysis of a complex data set that advances understanding in a related field, such as public health, economics, government, or genetics. Such a thesis may rely entirely on existing methods, but should give useful results and insights into an interesting applied problem.

3. An analysis of a complex data set in which new methods or modifications of published methods are required. While the thesis does not necessarily contain an extensive mathematical study of the new methods, it should contain strong plausibility arguments or simulations supporting the use of the new methods.

A good thesis is clear, readable, and well-motivated, justifying the applicability of the methods used rather than, for example, mechanically running regressions without discussing the assumptions (and whether they are plausible), performing diagnostics, and checking whether the conclusions make sense.

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A Grand Journey of Statistical Hierarchical Modelling

Advances in empirical bayes modeling and bayesian computation , advances in statistical network modeling and nonlinear time series modeling , advances in the normal-normal hierarchical model , analysis, modeling, and optimal experimental design under uncertainty: from carbon nano-structures to 3d printing , bayesian biclustering on discrete data: variable selection methods , bayesian learning of relationships , a bayesian perspective on factorial experiments using potential outcomes , building interpretable models: from bayesian networks to neural networks , causal inference under network interference: a framework for experiments on social networks , complications in causal inference: incorporating information observed after treatment is assigned , diagnostic tools in missing data and causal inference on time series , dilemmas in design: from neyman and fisher to 3d printing , distributed and multiphase inference in theory and practice: principles, modeling, and computation for high-throughput science , essays in causal inference and public policy , expediting scientific discoveries with bayesian statistical methods , exploring objective causal inference in case-noncase studies under the rubin causal model , exploring the role of randomization in causal inference , extensions of randomization-based methods for causal inference , g-squared statistic for detecting dependence, additive modeling, and calibration concordance for astrophysical data .

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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]

Princeton University Doctoral Dissertations, 2011-2024
Operations Research and Financial Engineering

Title:	Statistical Methods in Finance
Authors:
Advisors:
Contributors:	Operations Research and Financial Engineering Department
Keywords:
Subjects:
Issue Date:	2014
Publisher:	Princeton, NJ : Princeton University
Abstract:	This dissertation focuses on statistical methods in finance, with an emphasis on the theories and applications of factor models. Past studies have generated fruitful results applying statistical techniques in various cross-sectional and time-series analyses, yet better econometric methods are always called for to deal with more involved financial economic settings. To start with, ultra-large data sets which contain high-dimensional variables are increasingly common in recent decades, and make the initial screening of factors both important and necessary. In Chapter 1, a nonparametric independence screening method is proposed for high-dimensional varying coefficient models, a broad class of models used to explore the dynamic impact of factors that evolves over time or with certain characteristics. Another challenge facing financial research is the search and interpretation of factors especially when the underlying process is more volatile. With the 2008 financial crisis included in the period of study, Chapter 2 identifies the risk factors of the volatility risk premium in financial markets, and provides insight into how investors hedge their downside risk and how market intermediates provide liquidity. Meanwhile, the way proxy for factors is chosen may also play an important role in financial studies. We analyze in Chapter 3 how our proposed statistic, the fraction of forecasts that miss on the same side, better measures the market surprise than traditional consensus error, and show its power in capital market event studies. Finally, conventional approaches may no longer be robust when some factors are unobserved, as in the case of risk adjusted fund evaluation. In Chapter 4, we propose a method to more precisely evaluate mutual fund performance in the presence of herding effects and latent factors, and the results improve our understanding of what fraction of fund managers are truly generating alphas.
URI:
Alternate format:	The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the
Type of Material:	Academic dissertations (Ph.D.)
Language:	en
Appears in Collections:

File	Description	Size	Format
Dai_princeton_0181D_10917.pdf		2.5 MB	Adobe PDF

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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.

Apply to GW

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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
	Mathematical Statistics I
	Mathematical Statistics II
	Bayesian Statistics: Theory and Applications
	Probability
	Distribution Theory
	Advanced Statistical Theory I
	Advanced Statistical Theory II
At least two of the following:
	Linear Models
	Advanced Biostatistical Methods
	Advanced Probability
	Nonparametric Inference
	Multivariate Analysis
	Stochastic Processes I
	Stochastic Processes II
	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:
	Mathematical Statistics I
	Mathematical Statistics II
	Probability
	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.

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
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Natural Sciences and Mathematics

Mathematical sciences.

$Trellis geometry$

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

Recent PhD Theses

Additional theses can be found on UWSpace .

| 2020 | 2021 | 2022 | 2023 | 2024


Jiao, Zhanyi (Wang, R.)
Hagar, Luke (Stevens, N.)
Lin, Liyuan (Schied, A.)
Jian, Jie (Sang, P., Zhu, M.)
Hou, Zhaoran (Eric) (Wong, S.)
Bui, Trang (Steiner, S., Stevens, N.)
Lashkari, Banefsheh (Chenouri, S.)
Salahub, Chris (Olford, W.)
Wang, Lijia (Zhu, Y., Cook, R.)


Panahi, Mahsa (Steiner, S.)
Mussavi Rizi, Marzieh (Dubin, J.)
Yin, Mingren (Cai, J.)
Yeh, Chi-Kuang (Rice, G.)
Sharp, Alexander (Browne, R.)
Jiang, Ruihong (Weng, C.)
Hou-Liu, Jason (Browne, R.)
Wang, Qiuqi (Wang, R)
Pirnia, Shahab (Chenouri, S.)
Araiza Iturria, Carlos Andres (Hardy, M., Marriott, P.)
Li, Wenyuan (Hardy, M., Seng Tan, K., Wei, P.)
Sun, Zhaohan (Zhu, Y., Dubin, J.)
Shuldiner, Pavel (Oldford, W.)


Meng, Yechao (Weng, C., Diao, L.)
Qi, Weinan (Marriott, P., Shen, Y.)
Chen, Yuyu (Wang, R.)
Spicker, Dylan (Zachary) (Wallace, M., Yi, G.)
Jiang, Cong (Wallace, M., Thompson, M.)
Dong, Gracia (Lemieux, C.)
Kim, Nam-Hwui (Browne, R.)
Shum, Marco Yan Shing (Marriott, P.)
Xu, Zehao (Oldford, W.)
Ramsay, Kelly (Chenouri, S.)
Zhu, Feiyu (Lysy, M.)
Wang, Zijia (Landriault, D.)
Hintz, Erik (Lemieux, C.)
Mao, Fangya (Cook, R.)
Cheng, Lu (Zhu, M.)


Xie, Yijun (Rice, G., Kolkiewicz, A.)
Zhuang, Haoxin (Diao, L., Yi, G.)
Panju, Maysum (Ghodsi, A.)
Dang, Ou (Hardy, M.)
Sucholutsky, Ilia (Schonlau, M.)
Wang, Yumin (Landriault, D.)
Yang, Ce (Cook, R.)
Xie, Bing Feng (Cook, R.)
Yuan, Meng (Li, P.)


Tian, Zhaoyang (Li, P., Liang, K.)
Shi, Yidan (Thomson, M., Zeng, L.)
Koike, Takaaki (Hofert, M.)
Prasad, Avinash Srikanta (Hofert, M., Zhu, M.)
Meng, Fei (Saunders, D.)
Zhang, Qihuang (Yi, G.)
Chen, Yilin (Wu, C.)
Fang, Junhan (Yi, G.)
Qiao, Rui (Ghodsi, A.)
Che, Menglu (Han, P., Lawless, J.)
Cao, Jingyi (Landriault, D., Bin, L.)
He, Zhoushanyue (Schonlau, M.)
Jia, Huameng (Cai, J. )

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) OR Co-Advisor†	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.

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	A content analysis of advertiser influence on editorial content in fashion magazines \| M.S. \| 05/2010
	Derivation of the complete transcriptome of Escherichia coli from microarray data \| M.S. \| 12/2009
	The coordination of design and analysis techniques for functional magnetic resonance imaging data \| M.S. \| 05/2009
	A review of ruin probability models \| M.S. \| 12/2009
	The exploration of statistical ensemble methods for market segmentation \| M.S. \| 05/2009
	Misidentification error in non-invasive genetic mark-recapture sampling: case study with the central Georgia black bear population \| M.S. \| 05/2009
	A time series analysis of mortality and air pollution in Hong Kong from 1997 to 2007 \| M.S. \| 05/2009
	Penalized principal component regression \| M.S. \| 05/2008
	Statistical methods for turtle bycatch data \| M.S. \| 12/2008
	Sexual dysfunction in young women with breast cancer \| M.S. \| 12/2008
	Investigation of statistical methods for determination of benchmark dose limits for retinoic acid-induced fetal forelimb malformation in mice \| M.S. \| 12/2008
	Competing risk models for turtle nest survival in the Bolivian Amazon \| M.S. \| 05/2008
	Exploring bidder characteristics in online auctions: an application of a bilinear mixed model to study overbidders \| M.S. \| 08/2007
	Baseball prediction using ensemble learning \| M.S. \| 05/2007
	Adoption and use of Internet among American organic farmers \| M.S. \| 12/2007
	Population structure of loggerhead sea turtles (Caretta caretta) nesting in the southeastern United States inferred from mitochondrial DNA sequences and microsatellite loci \| M.S. \| 05/2007
	Small-sample prediction of estimated loss potentials \| M.S. \| 12/2007
	Applications for NIR spectroscopy in eucalyptus genetics improvement programs and pulp mill operations \| M.S. \| 12/2007
	Lq penalized regression \| M.S. \| 05/2007
	Estimating the demand for and value of recreation access to national forest wilderness: a comparison of travel cost and onsite cost day models \| M.S. \| 05/2007
	Implementing SELC (sequential elimination of level combinations) for practitioners: new statistical softwares \| M.S. \| 12/2006
	GIS-based habitat modeling related to bearded Capuchin monkey tool use \| M.S. \| 08/2006
	Historic airboat use and change assessment using remote sensing and geographic information system techniques in Everglades National Park \| M.S. \| 08/2006
	An evaluation of airbags \| M.S. \| 05/2005
	Mixed effects models for a directional response: a case study with loblolly pine microfibril angle \| M.S. \| 08/2005
	Cross-nation examination of CCI and CPI with an emphasis on Korea \| M.S. \| 05/2005
	A new nonparametric bivariate survival function estimator under random right censoring \| M.S. \| 05/2005
	Forecasting crop water demand: structural and time series analysis \| M.S. \| 08/2004
	Extreme value methods in body-burden analysis: with application to inference from long-term data sets \| M.S. \| 05/2004
	Development of a screening method for determination of aflatoxins \| M.S. \| 12/2004
	Regression models in standardized test prediction \| M.S. \| 08/2004
	Comparison between frequentist and Bayesian implementation of mixed linear model for analysis of microarray data \| M.S. \| 05/2004
	Temporal autocorrelation in modeling soil potentially mineralizable nitrogen \| M.S. \| 05/2004
	Using extreme value models for analyzing river flow \| M.S. \| 08/2004
	Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables \| M.S. \| 08/2004
	Monitoring expense report errors: control charts under independence and dependence \| M.S. \| 05/2004
	Time series analysis of volatility in financial markets in Hong Kong from 1991 to 2004 \| M.S. \| 12/2004
	Predictive modeling of professional figure skating tournament data \| M.S. \| 08/2003
	Statistical dimension reduction methods for appearance-based face recognition \| M.S. \| 05/2003
	Statistical analysis of 16s rdna gene-based intestinal bacteria in chickens \| M.S. \| 12/2003
	Reconstruction of early 19th century vegetation to assess landscape change in southwestern Georgia \| M.S. \| 12/2003
	Statistical model for estimating the probability of using electronic cards : a statistical analysis of SCF data \| M.S. \| 08/2003
	A survey of Hill's estimator \| M.S. \| 08/2003
	Statistical analysis of mass spectrometry-assisted protein identification methods \| M.S. \| 12/2003
	Intra-individual variation in serum vitamin A measures among participants in the Third National Health and Nutrition Examination Survey, 1988-1994 \| M.S. \| 05/2002
	Application and comparison of time series models to AIDS data \| M.S. \| 05/2002
	Are wealthier elderly healthier? : a statistical analysis of AHEAD data \| M.S. \| 08/2002
	Statistical modeling and analysis of the polymerase chain reaction \| M.S. \| 05/2002
	Statistical model for the diffusion of innovation and its applications \| M.S. \| 12/2002
	Spatial pattern analysis and modeling of Heterotheca subaxillaris and Lespedeza cuneata in a South Carolina old-field \| M.S. \| 08/2002
	Prediction of residential mortgage contract rates \| M.S. \| 05/2002
	Palmist: a tool to log Palm system activity \| M.S. \| 12/2001
	The grilseification of Atlantic salmon in Iceland \| M.S. \| 08/2001
	Stochastic volatility models: a maximum likelihood approach \| M.S. \| 08/2000

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Statistics and Actuarial Science

Graduate theses.

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Below is a list of the theses produced by graduate students in the Department of Statistics and Actuarial Science.


2023-3	Payman Nickchi	Ph.D	Linkage fine-mapping on sequences from case-control studies and Goodness-of-fit tests based on empirical distribution function for general likelihood model	R. Lockhart & J. Graham
2023-3	Gurashish Bagga	MSc	Offensive and defensive penalties on score differentials and drive outcomes in the NFL	J. Hu
2023-3	Rina Wang	MSc	The Application of Categorical Embedding and Spatial-Constraint Clustering Methods in Nested GLM Model	J. Cao
2023-3	David (Liwei) Lai	MSc	An Exploration of a Testing Procedure for the Aviation Industry	T. Swartz & G. Parker
2023-3	Teng-Wei Lin	MSc	Forecasting the trajectories of Southern Resident Killer Whales with stochastic continuous-time movement models	R. Joy & R. Routledge
2023-3	Nirodha Epasinghege Dona	PhD	Big Data Applications in Genetics and Sports	J. Graham & T. Swartz
2023-3	Kim Kroetch	MSc		D. Estep
2023-3	Summer Shan	MSc		C. Tsai
2023-3	William Ruth	PhD		R. Lockhart
2023-2	Boyi Hu	PhD		J. Cao
2023-2	Trevor Thomson	PhD		J. Hu
2023-2	Daisy (Ying) Yu	PhD		B. McNeney
2023-2	Pulindu Ratnasekera	PhD		B. McNeney
2023-2	Yuqi Meng	MSc		T. Loughin
2023-2	Linwan Xu	MSc		J. Hu
2023-2	Manpreet Kaur	MSc		B. Tang
2023-2	Guanzhou Chen	PhD		B. Tang
2023-2	Kalpani Darsha Perera	MSc		B. Tang
2023-2	Junpu Xie	MSc		D. Estep
2023-2	Haixu Wang	PhD		J. Cao
2023-2	Jesse Schneider	MSc		D. Stenning
2023-1	Tianyu Yang	MSc		J. Graham
2023-1	Hashan Peiris	MSc		H. Jeong
2023-1	Yaning Zhang	MSc		Y. Lu
2022-3	Elijah Cavan	MSc		T. Swartz & J. Cao
2022-3	Carla Louw	MSc		R. Lockhart
2022-3	Wenyuan Zhou	MSc		J. Bégin & B. Sanders
2022-3	Ryker Moreau	MSc		H. Perera & T. Swartz
2022-3	Lucas (Yifan) Wu	PhD		T. Swartz
2022-3	Shaun McDonald	PhD		D. Campbell
2022-2	Luyao Lin	PhD		D. Bingham
2022-2	Youwei Yan	MSc		D. Stenning
2022-2	Lei Chen	MSc		Y. Lu
2022-2	Jacob (Xuankang) Zhu	MSc		D. Estep
2022-2	Hasan Nathani	MSc		C. Tsai
2022-2	Mandy Yao	MSc		D. Estep
2022-1	Zayed Shahjahan	MSc		J. Graham
2022-1	Menqi (Molly) Cen	MSc		J. Hu
2022-1	Wen Tian (Wendy) Wang	MSc		B. Tang
2022-1	Yazdi Faezeh	PhD		D. Bingham
2022-1	Winfield Chen	MSc		L. Elliott
2021-3	Kangyi (Ken) Peng	MSc		T. Swartz & G. Parker
2021-3	Xueyi (Wendy) Xu	MSc		B. Sanders
2021-3	Christina Nieuwoudt	PhD		J. Graham
2021-2	Yige (Vivian) Jin	MSc		J.F. Bégin
2021-2	Peter Tea	MSc		T. Swartz
2021-2	Louis Arsenault-Mahjoubi	MSc		J.F. Bégin
2021-2	Cheng-Yu Sun	PhD		B. Tang
2021-2	Xuefei (Gloria) Yang	MSc		B. McNeney
2021-2	Charith Karunarathna	PhD		J. Graham
2021-1	Lisa McQuarrie	MSc		R.Altman
2021-1	Yunwei Tu	MSc		R.Lockhart
2021-1	Nikola Surjanovic	MSc		T. Loughin
2020-3	Renny Doig	MSc		L.Wang
2020-3	Dylan Maciel	MSc		D.Bingham
2020-3	Cherie Ng	MSc		J.F. Bégin
2020-3	James Thomson	MSc		G.Perera
2020-2	Gabriel Phelan	MSc		D. Campbell
2020-2	Jacob Mortensen	PhD		L. Bornn
2020-2	Yi Xiong	PhD		J. Hu
2020-2	Shufei Ge	PhD		L. Wang
2020-2	Fei Mo	MSc		J.F. Bégin
2020-2	Tainyu Guan	PhD		J. Cao
2020-2	Haiyang (Jason) Jiang	MSc		T. Loughin
2020-2	Nathan Sandholtz	PhD		L. Bornn
2020-2	Zhiyang (Gee) Zhou	PhD		R. Lockhart
2020-2	Matthew Reyers	MSc		T. Swartz
2020-2	Jie (John) Wang	MSc		L. Wang
2020-1	Matt Berkowitz	MSc		R. Altman
2020-1	Megan Kurz	MSc		J. Hu
2020-1	Siyuan Chen	MSc		B. McNeney
2020-1	Sihan (Echo) Cheng	MSc		C. Tsai
2020-1	Barinder Thind	MSc		J. Cao
2020-1	Neil Faught	MSc		S. Thompson
2020-1	Kanav Gupta	MSc		J.F. Bégin
2020-1	Dani Chu	MSc		T. Swartz

Projects and Theses From Previous Years

2015 - 2019 2010 - 2014 2005 - 2009 2000 - 2004 1990's 1980's and prior

Graduate Talks

Statistics Ph.D. Dissertation Defense - Ciara Nugent

Description.

This 2024 Dissertation Defense will be held on Friday, July 26, from 8:30 a.m. to 10:30 a.m. with Ciara Nugent. This event will be virtual. If you need the Zoom link, please email [email protected].

Title : A Decision Theoretic Approach to Combining Inference Across Data Sources with Applications to Subgroup Analysis in Clinical Trials

Advisor : Peter Mueller

Abstract : Many research questions can not be answered by a single scientific study or source of data. To address this challenge, researchers develop principled ways to combine information from multiple studies to reach a conclusion. A very common instance of this problem is the combination of results across multiple clinical studies, commonly known as meta-analysis, usually with the understanding that results from individual studies are only available as published summary statistics. In this thesis I consider principled Bayesian decision theoretic approaches to the general problem of combining inference across multiple studies. After a discussion of the general problem I focus on the particular example that arises when the desired inference is subgroup analysis, that is, inference about how results for subpopulations of interest differ from the results of the larger study population. In the last chapter I consider an extension to a typical meta-analysis problem. Methodologically, the proposed solutions start with a decision theoretic approach to combining inference from two studies through the use of a utility function. Building on this foundation I then consider a more complex utility function for Bayesian population finding. Finally, expanding upon the same utility function I then consider more than two studies to propose a similar solution for a general meta-analysis problem.

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Other Events in This Series

Statistics ph.d. dissertation defense - shuying wang.

Bayesian Inference for Stochastic Compartmental Models and Marginal Cox Process

11:00 am – 1:00 pm • In Person

Speaker(s): Shuying Wang

Statistics Ph.D. Dissertation Defense - Huangjie Zheng

Implicit Distributional Matching at High Dimensionality

11:00 am – 1:00 pm • Virtual

Speaker(s): Huangjie Zheng

Statistics Ph.D. Dissertation Defense - Rimli Sengupta

Semi-Parametric Generalized Linear Models in Novel Analytical Contexts

9:45 am – 11:45 am • In Person

Speaker(s): Rimli Sengupta

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Department of Statistics: Dissertations, Theses, and Student Work
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.
Doctoral Program
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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.
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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 ...
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MS Theses
MS Theses. We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.
Graduate Theses
Below is a list of the theses produced by graduate students in the Department of Statistics and Actuarial Science. 2023-3 Payman Nickchi Ph.D Linkage fine-mapping on sequences from case-control studies and Goodness-of-fit tests based on empirical distribution function for general likelihood model R ...
Statistics Ph.D. Dissertation Defense
Graduate Talks. Statistics Ph.D. Dissertation Defense - Huangjie Zheng. Implicit Distributional Matching at High Dimensionality 11:00 am - 1:00 pm &bullet; Virtual Speaker(s): Huangjie Zheng . Jul. 31. 2024. Graduate Talks. Statistics Ph.D. Dissertation Defense - Rimli Sengupta. Semi-Parametric Generalized Linear Models in Novel Analytical ...

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