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Home > Statistics > Dissertations, Theses, and Student Work
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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Program summary.
Students are required to
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 .
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 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.
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.
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 .
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.
Dissertation Advisor: Martin Wells
Initial job placement: Co-Founder & CEO
Dissertation Advisor: David Ruppert
Initial job placement: VISITING ASSISTANT PROFESSOR - Cornell University
Dissertation Advisor: David Matteson
Initial job placement: Postdoctoral Fellow - UCLA
Dissertation Advisor: Yang Ning
Initial job placement: Data Scientist - TikTok
Initial job placement: Quantitative Researcher - DRW
Initial job placement: Data Scientist - Research at Google
Dissertation Advisor: Michael Nussbaum
Initial job placement: Postdoctoral Fellow - Harvard University
Dissertation Advisor: Jim Dai
Initial job placement: Applied Scientist - Amazon
Dissertation Advisor: Marten Wegkamp and Florentina Bunea
Initial job placement: Data Scientist at Google
Dissertation Advisor: David Ruppert and Kengo Kato
Initial job placement: Quantitative Researcher - Point72
Initial job placement: Ernst & Young
Initial job placement: Amazon
Dissertation Advisor: Thorsten Joachims
Initial job placement: Berkeley (postdoc)
Dissertation Advisor: Christopher De Sa
Initial job placement: LinkedIn
Dissertation Advisor: Giles Hooker
Initial job placement: Rochester (postdoc)
Dissertation Advisor: David Matteson and Joe Guinness
Initial job placement: Institute for Defense Analysis
Initial job placement: Facebook
Initial job placement: Microsoft
Dissertation Advisor: Nathan Kallus and Madeleine Udell
Initial job placement: Tsinghua University, China
Initial job placement: Cambridge (postdoc), University of Toronto
Dissertation Advisor: David Ruppert and Peter Frazier
Initial job placement: Research Analyst - Cubist Systematic Strategies
Initial job placement: Machine Learning Researcher - Apple
Initial job placement:
Initial job placement: Research Scientist - Institute for Infocomm Research
Initial job placement: Quantitative Researcher - Two Sigma
Dissertation Advisor: James Booth and Martin Wells
Dissertation Advisor: Andrew Wilson
Initial job placement: AI Scientist - AWS AI Labs
Dissertation Advisor: Sumanta Basu and Madeleine Udell
Dissertation Advisor: Giles Hooker
Initial job placement: Data & Applied Scientist - Microsoft
Dissertation Advisor: Giles Hooker and Martin Wells
Yichen zhou - “asymptotics and interpretability of decision trees and decision tree ensemblesg”.
Initial job placement: Data Scientist - Google
Initial job placement: Research Scientist, Facebook Integrity Ranking & ML - Facebook
Dissertation Advisor: Jacob Bien
Initial job placement: Senior Data Scientist - Microsoft
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
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
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
Dissertation Advisor: James Booth
Initial Job Placement: Assistant Professor, Texas A&M, College Station, TX
Initial Job Placement: Assistant Professor, University of Pittsburgh, Pittsburgh, PA
Dissertation Advisors: David Matteson and David Ruppert
Initial Job Placement: Postdoctoral Fellow, University of North Carolina, Chapel Hill, NC
Disseration Advisor: Marten Wegkamp
Initial Job Placement: Postoctoral Fellow, McGill University, Montreal, Canada
Dissertation Advisor: Martin Wells
Dissertation Advisor: Giles Hooker
Initial Job Placement: Lecturer, Cornell University, Ithaca, NY
Initial Job Placement: Research Scientist, Yahoo Labs
Dissertation Advisor: John Abowd
Initial Job Placement: Assistant Professor, Northwestern University, Evanston, IL
Initial Job Placement: Data Scientist, Memorial Sloan Kettering Cancer Center, New York, NY
Initial Job Placement: Senior Associate, 1010 Data, New York, NY
Dissertation Advisor: Robert Strawderman
Initial Job Placement: Statistical Consultant, Cornell, Statistical Consulting Unit, Ithaca, NY
Initial Job Placement: Postdoctoral Fellow, Georg-August-Universitat Gottigen, Gottigen, Germany.
Dissertation Advisor: James Booth
Initial Job Placement: Postdoc, Department of Medicine, Weill Medical Center, New York, NY
Initial Job Placement: Assistant Professor, Le Moyne College, Syracuse, NY
Dissertation Advisor: Michael Nussbaum
Initial Job Placement: Assistant Professor, University of Georgia, Athens, GA
Dissertation Advisor: Francesca Molinari
Initial Job Placement: Research Mathematical Statistician, Center for Statistical Research and Methodology, U.S. Census Bureau, Washington DC
Initial Job Placement: Visiting Scientist, Indian Statistical Institute, Kolkata, India
Dissertation Advisor: David Ruppert
Initial Job Placement: Postdoc, Johns Hopkins University, Baltimore, MD
Dissertation Advisor: Sidney Resnick
Initial Job Placement: Data Analyst, Mozilla, San Francisco, CA
Dissertation Advisor: Robert Strawderman
Initial Job Placement: Quantitative Analyst, Smartodds, London UK
Dissertation Advisor: Carlos Bustamante
Initial Job Placement: Biostatistician, The J. David Gladstone Institutes, San Francisco CA
Dissertation Advisor: Gennady Samorodnitsky
Initial Job Placement: Assistant Professor, UNC Charlotte, Charlotte NC
Initial Job Placement: Assistant Professor, The Wang Yanan Institute for Studies in Economics, Xiamen University
Dissertation Advisor: Sidney Resnick
Initial Job Placement: Consultant, Murex North America, New York NY
Initial Job Placement: Postdoc, Stanford University, Stanford CA
Dissertation Advisor: James Booth
Initial Job Placement: Postdoc, Department of Public Health, Weill Cornell Medical College, New York NY
Initial Job Placement: Postdoc, Department of Biostatistics, Harvard University, Boston MA
Dissertation Advisor: Anand Vidyashankar
Initial Job Placement: Postdoc, SAMSI, Durham NC
Initial Job Placement: Postdoc, Department of Biostatistics, Johns Hopkins University, Baltimore MD
Dissertation Advisor: John Bunge
Initial Job Placement: Pfizer Incorporated
Initial Job Placement: Statistician, Takeda Pharmaceuticles, Deerfield IL
Dissertation Advisor: Martin Wells
Initial Job Placement: Associate, Fixed Income Department, Credit Suisse Securities (USA), New York, NY
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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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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 |
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.
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.
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):
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.
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.
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.
4c69b3a36a33a4c1c5b5cd3ef5360949.
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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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
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
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.
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:
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.
The following core courses are required for all Ph.D. students:
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.
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.
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]
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 | |
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Dai_princeton_0181D_10917.pdf | 2.5 MB | Adobe PDF |
Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.
Department of Statistics
Columbian College of Arts & Sciences
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
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.
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:
The written exam is required for the first attempt. If a student cannot pass it, then there are two options for the second attempt.
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.
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
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 |
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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. |
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.
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.
In order to follow best practices, some first steps need to be followed. Here is a list of things to do:
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.
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.
Mathematical sciences.
Year of Graduation | Student | Supervising Professor | Dissertation Title |
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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 |
Additional theses can be found on UWSpace .
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Bui, Trang (Steiner, S., Stevens, N.) | |
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Hou-Liu, Jason | |
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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.) |
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Spicker, Dylan (Zachary) (Wallace, M., Yi, G.) | |
Jiang, Cong | |
Dong, Gracia (Lemieux, C.) | |
Kim, Nam-Hwui | |
Shum, Marco Yan Shing |
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Yuan, Meng |
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Tian, Zhaoyang |
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Shi, Yidan |
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Koike, Takaaki |
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Prasad, Avinash Srikanta |
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Cao, Jingyi |
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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.
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.
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 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.
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:
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 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 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 .
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).
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.
Important milestones: .
Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO. If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding (including GSI or GSR appointments, and many fellowships).
Identify PhD Advisor† | End of 2nd year |
Identify Research Mentor (QE Chair) | Fall semester of 3rd year |
Pass Qualifying Exam and Advance to Candidacy | End of 3rd year |
Thesis Submission | End of 4th or 5th year |
†Students who are considering a co-advisor, should have at least one advisor formally identified by the end of the second year; the co-advisor should be identified by the end of the fall semester of the 3rd year in lieu of finding a Research Mentor/QE Chair.
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.
| Using the sir epidemic model to infer the SARS outbreak in Beijing, 2003 | M.S. | 05/2019 |
| Data mining to identify gene regulatory elements | M.S. | 05/2019 |
| A comparison between logistic regression and neural networks in a constructed response item study | M.S. | 05/2019 |
| The effect of CEO political ideology on executive succession following firm misconduct | M.S. | 05/2019 |
| Analyses of USA birth-date distribution | M.S. | 08/2019 |
| Sawtimber potential proportion dynamics for loblolly pine in the Southeastern U.S. | M.S. | 08/2019 |
| Scalable individual planning in open and typed agent systems | M.S. | 05/2019 |
| Three different approaches of missing data imputation for financial data | M.S. | 05/2019 |
| A statistical approach for calibrating hydrologic models | M.S. | 05/2018 |
| Prediction of peanut butter prices in the United States by tracking concept drift | M.S. | 05/2018 |
| An application of semiparametric model estimation under shape invariance to fmri data | M.S. | 08/2018 |
| Regularized aggregation of multiple graphs with application to FMRI data | M.S. | 12/2018 |
| Model comparison with squared sharpe ratios of mimicking portfolios | M.S. | 05/2018 |
| Efficient genotyping by sampling extreme individuals in a genome wide association study in plants | M.S. | 05/2018 |
| A statistical analysis of some aspects of well-being of South Korean elderly population | M.S. | 05/2018 |
| A statistical analysis of crime in San Luis Obispo (2009-2017) | M.S. | 05/2018 |
| An expected outcome framework for evaluating batting and pitching performance in major league baseball with applications to the "juiced ball" and the "fly ball revolution" | M.S. | 05/2018 |
| Modelling precipitation volumes using a weibull mixture and the gamma generalized linear model | M.S. | 12/2018 |
| Normal and average: lexical ambiguity in an introductory statistics course | M.S. | 12/2018 |
| The periodic solution and the global asymptotic stability for northeastern Puerto Rico ecosystem | M.S. | 08/2018 |
| An examination of the transfer of errors to species tree estimation caused by model selection in gene tree estimation | M.S. | 05/2017 |
| Assessment of the performance of the lasso algorithm compared to the k-nn algorithm with high-dimensional class imbalanced data | M.S. | 05/2017 |
| A comparison of pedagogical approaches in introductory statstics | M.S. | 05/2017 |
| A mixed effect model with feature extraction for functional magnetic resonance imaging (fMRI) data | M.S. | 05/2017 |
| Assessing UGA transportation and parking services data collection using RouteMatch Software? | M.S. | 05/2017 |
| Analyzing android ad-libraries | M.S. | 12/2017 |
| Generative spatiotemporal modeling of neutrophil behavior | M.S. | 12/2017 |
| An application of graphical models to fMRI data using the lasso penalty | M.S. | 05/2017 |
| Skew and bias: the efficacy of an intervention in an introductory statistics course | M.S. | 05/2017 |
| Bootstrap based measurement of serial correlation in time series objects | M.S. | 12/2017 |
| Copula modeling analysis on multi-dimensional portfolios with backtesting | M.S. | 08/2016 |
| Data analysis of the pattern information of the collective decision-making process in subterranean termites species | M.S. | 08/2016 |
| Modeling NFL quarterback success with college data | M.S. | 05/2016 |
| Comparison of data sampling methods on IRT parameter estimation | M.S. | 05/2016 |
| Estimating precipitation volume distributions using data from the spatially dense cocorahs network | M.S. | 08/2016 |
| Predictive biomarker reproducibility modeling with censored data | M.S. | 12/2016 |
| Parallel matrix factorization in big data analytics | M.S. | 08/2016 |
| Maximum monthly rainfall behavior along the front range of Colorado | M.S. | 12/2016 |
| A study on adaptive lasso and its weight selection | M.S. | 12/2016 |
| Predictors of secondary traumatic stress among clinical social workers: a focus on the impact of the supervisory relationship | M.S. | 05/2016 |
| Estimating nutrient uptake in streams with pulse release | M.S. | 12/2016 |
| Prediction of crime categories in San Francisco area | M.S. | 05/2016 |
| Perceived importance and objective measures of built environment walkability of a university campus | M.S. | 05/2016 |
| Mergers and network effects: understanding the recent increase in percentage of non-weather-caused flight delays in the United States | M.S. | 05/2015 |
| A rolling analysis on the prediction of Value at Risk with multivariate GARCH and copula | M.S. | 05/2015 |
| Analysis of climate-crop yield relationships in Canada with distance correlation | M.S. | 12/2015 |
| False negative control for multiple acceptance-support hypotheses testing problem | M.S. | 05/2015 |
| Big data analytic tools to detect fraud in healthcare data | M.S. | 12/2015 |
| Genetic algorithms developed in R software for finding optimal experimental designs | M.S. | 05/2015 |
| Bootstrap-based test for volatility shifts in GARCH against long-range dependence | M.S. | 05/2015 |
| A rule-engine-based application for over-the-counter medication safety | M.S. | 12/2014 |
| Household whole and low-fat milk consumption in Poland: a censored system approach | M.S. | 12/2014 |
| Calibrating test item banks for an introductory statistics course | M.S. | 05/2014 |
| A guide and solution manual to The elements of statistical learning | M.S. | 12/2014 |
| Programmatic assessment for an undergraduate statistics major | M.S. | 05/2014 |
| Global temperature trends | M.S. | 08/2014 |
| Penalized regression models for Major League Baseball metrics | M.S. | 05/2014 |
| Discriminant function analysis of Major League Baseball steroid use | M.S. | 05/2014 |
| Estimation of government employment using multivariate hierarchical Bayes modeling | M.S. | 05/2014 |
| Feasibility of small voxel sizes in canine brain 1H-magnetic resonance spectroscopy at 3T | M.S. | 08/2014 |
| Improving the robustness of turbulent fluxes: an examination of the role of waves on fluxes and turbulence statistics | M.S. | 08/2014 |
| Phylogenetic analysis of cancer microarray data | M.S. | 12/2014 |
| Students? misconceptions about introductory statistics topics: assessing STAT 2000 outcomes using CAOS | M.S. | 05/2013 |
| The use of bootstrapping to measure image differences in fMRI data | M.S. | 05/2013 |
| Performance of farm level vs area level crop insurance | M.S. | 08/2013 |
| Application of multivariate geospatial statistics to soil hydraulic properties | M.S. | 12/2013 |
| Characterizing the socioeconomics of metropolitan transportation network expansion by mining a nationwide road change database | M.S. | 05/2013 |
| The rise of the Big Data: why should statisticians embrace collaborations with computer scientists | M.S. | 12/2013 |
| Undergraduate students? attitudes toward statistics in an introductory statistics class | M.S. | 12/2013 |
| Comparison of methods of analysis for Pretest and Posttest data | M.S. | 08/2013 |
| Drought, biofuel, and livestock | M.S. | 12/2013 |
| A comparison of meta-analytic approaches on the consequences of role stressors | M.S. | 08/2013 |
| Improving validity and reliability in STAT 2000 assessments | M.S. | 05/2013 |
| Classification analysis in microarray data using biological pathway and gene family information | M.S. | 12/2013 |
| Predicting equity returns using Twitter sentiment | M.S. | 05/2013 |
| Monthly trends in maxima of low temperatures in Georgia, USA | M.S. | 05/2013 |
| HIV classification using DNA sequences | M.S. | 08/2013 |
| Double eQTL mapping method to improve identification of trans eQTLs and construct intermediate gene networks | M.S. | 05/2013 |
| CacheMeter | M.S. | 08/2013 |
| A study on expectiles: measuring risk in finance | M.S. | 12/2012 |
| Design of cost-fffective cancer biomarker reproducibility studies | M.S. | 08/2012 |
| Flux measurements in the stable boundary layer and during morning transition | M.S. | 12/2012 |
| Predicting outcomes of mixed martial arts fights with novel fight variables | M.S. | 08/2012 |
| Estimation in populations with rare events | M.S. | 05/2012 |
| A Bayesian hierarchical spatial model for West Nile Virus in New York City: evaluating an approach to handle large spatial data sets | M.S. | 12/2012 |
| The influence of measurement errors in tumor markers | M.S. | 12/2012 |
| Statistical interpretation of experiments with laying hens | M.S. | 05/2012 |
| Estimation of genomic copy frequency with correlated observations | M.S. | 05/2012 |
| The appearance of Michelle Obama: an analysis of the First Lady's exposure in magazines, from January 2008 to December 2009 | M.S. | 05/2012 |
| Case studies of clear-air turbulence: evaluation and verification of new forecasting techniques | M.S. | 08/2012 |
| Assessment of nonparametric frontier models applied to socially responsible investment | M.S. | 08/2011 |
| Nonparametric GARCH models for financial volatility | M.S. | 08/2011 |
| Investigating some estimators of the fractional degree of differencing, in long memory time series | M.S. | 05/2011 |
| A bootstrap method for fitting a linear regression model to interval-valued data | M.S. | 05/2011 |
| Variable selection in longitudinal data with application to education | M.S. | 08/2011 |
| Conservation genetics of the red-cockaded woodpecker | M.S. | 05/2010 |
| Using regression based methods for time-constrained scaling of parallel processor computing applications | M.S. | 05/2010 |
| Statistical study of the decay lifetimes of the photo-excited DNA nucleobase Adenine | M.S. | 12/2010 |
| The interpretation of experiments with poultry | M.S. | 12/2010 |
| Statistical identification of the quinic acid responsive genes in Neurospora crassa | M.S. | 12/2010 |
| 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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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. 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 |
2015 - 2019 2010 - 2014 2005 - 2009 2000 - 2004 1990's 1980's and prior
Graduate Talks
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.
This virtual event requires software to participate. Get help with Zoom or Microsoft Teams.
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
Implicit Distributional Matching at High Dimensionality
11:00 am – 1:00 pm • Virtual
Speaker(s): Huangjie Zheng
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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PhD Theses. 2024. Title. Author. Supervisor. Estimation and Inference of Optimal Policies. Zhaoqi Li. Alex Luedtke, Lalit Kumar Jain. Statistical Learning and Modeling with Graphs and Networks.
Theses/Dissertations from 2016 PDF. A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida, Joy Marie D'andrea. PDF. Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize, Sherlene Enriquez-Savery. PDF. Putnam's Inequality and Analytic Content in the Bergman Space, Matthew Fleeman. PDF
Dissertation TBA. Sponsor: Sumit Mukherjee. 2021 Ph.D. Dissertations. Tong Li. On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods. Sponsor: Liam Paninski. Ding Zhou. Advances in Statistical Machine Learning Methods for Neural Data Science. Sponsor: Liam Paninski.
PhD candidates: You are welcome and encouraged to deposit your dissertation here, but be aware that 1) it is optional, not required (the ProQuest deposit is required); and 2) it will be available to everyone online; there is no embargo for dissertations in the UNL Digital Commons. Master's candidates: Deposit of your thesis or project is required.
The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by ...
2015. 2014. 2013. 2012. 2011. 2010. 2009. 2008. This list of recent dissertation topics shows the range of research areas that our students are working on.
PhD in Statistics. Past PhD Theses; Graduate Syllabi; PhD Student Newsletter; Past PhD Theses Browse names and theses by graduation year. If you are an alumna or alumnus of the program, please visit the Alumni Outcomes page to learn more about how to stay involved. 2023. Jialu Wang.
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
PhD Dissertations. Junghee Bae. The commercial activity of nonprofit human service organizations analysis approach: latent class growth analysis approach | Ph. D. | 05/2019. Stephanie Marie Eick. Psychosocial stress among pregnant women in Puerto Rico | Ph. D. | 05/2019.
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.
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. ...
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 ...
Statistics is the art of communicating with the silent truth-teller: data. More legitimate, accurate and powerful inference from data is the endless pursuit of all statisticians. ... This thesis is divided into two self-contained parts. The first part focuses on diagnostic tools for missing data. Models for analyzing multivariate data sets with ...
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.
Statistics. Finance. 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 ...
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 ...
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 ...
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.
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 ...
Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests.
The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible ...
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.
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 ...
Graduate Talks. Statistics Ph.D. Dissertation Defense - Huangjie Zheng. Implicit Distributional Matching at High Dimensionality 11:00 am - 1:00 pm • 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 ...