Doctoral Program
The Statistics Department offers a Doctoral Program with two different tracks that students can choose from: a Mathematical Statistics track and a Biostatistics track. Both tracks are designed to provide doctoral candidates with a firm foundation on probability theory, inference, and classical methodology. In addition, the theory and application of statistical computing, and graphical as well as computer based inference are integral parts of the core curricula. The Biostatistics track provides students the opportunity to focus on one important area of statistical research, namely on the methodology and application of statistics in the life sciences. There is no direct link between the Mathematical Statistics and Biostatistics tracks in the MSc and the Doctoral programs, respectively. That is, a student can pursue a MSc in the Mathematical Statistics track, and then a PhD in the Biostatistics track.
The requirements for the two tracks are:
Mathematical Statistics/Probability
 STA 701 – Advanced Statistical Inference I
 STA 702 – Advanced Statistical Inference II
 STA 703 – Advanced Probability
 STA 705 – Advanced Computational Inference
 STA 707 – Advanced Data Analysis
Biostatistics
 STA 701  Advanced Statistical Inference I
 STA 703  Advanced Probability
 STA 705  Advanced Computational Inference
 STA 707  Advanced Data Analysis
 STA 709  Advanced Survival Analysis
All students must take an additional six courses chosen by the student and approved by the DGS. Three of these will complement and supplement the student's specialization area and research interests. STA 715 (Reading courses) may not be used to satisfy this requirement.
The six electives must be chosen from among STA 612, STA 616, STA 621, STA 624, STA 626, STA 630, STA 635, STA 643, STA 644, STA 653*, STA 661, STA 662, STA 665, CPH 631, CPH 636, and CPH 664*. STA 695 will also be considered on a case by case basis. A course that is required for the selected track may not be used as an elective.
* A student who takes both STA 653 and CPH 664, may only receive credit towards the degree for one of these two courses.
In the following, typical course schedules for both tracks are summarized. Several alternative paths are possible. It is clear from the above track requirements that the only difference in required courses is between STA 702 and STA 709. However, in practice, students often choose to take both of those classes, one for the track, and the other as an elective.
Mathematical Statistics Track: Typical Course Schedule
Fall, Year One  STA 602 Introduction to Statistical Methods  STA 605 Computational Inference  STA 623 Theory of Probability 
Spring, Year One  STA 603 Introduction to Linear Models and Experimental Design  STA 606 Theory of Statistical Inference I  STA 624 Applied Stochastic Processes 
Fall, Year Two  STA 607 Theory of Statistical Inference II  STA 643 Advanced Experimental Design  MA 471G or Elective 
Spring, Year Two  STA 632 Longitudinal Data Analysis  STA 700 Foundations of Probability and Inference  Elective 
Fall, Year Three  STA 701 Advanced Statistical Inference I  STA 703 Advanced Probability  STA 705 Advanced Computational Inference 
Spring, Year Three  STA 702 Advanced Statistical Inference II  STA 707 Advanced Data Analysis  Elective 
Fall, Year Four  Elective  Residency  Residency 
Spring, Year Four  Elective  Residency  Residency 
Biostatistics Track: Typical Course Schedule
Fall, Year One  STA 602 Introduction to Statistical Methods  STA 605 Computational Inference  STA 623 Theory of Probability 
Spring, Year One  STA 603 Introduction to Linear Models and Experimental Design  STA 606 Theory of Statistical Inference I  STA 653 Clinical Trials 
Fall, Year Two  STA 607 Theory of Statistical Inference II  STA 635 Survivability and Life Testing  STA 643 Advanced Experimental Design 
Spring, Year Two  STA 632 Longitudinal Data Analysis  STA 665 Analysis of Categorical Data  STA 700 Foundations of Probability and Inference 
Fall, Year Three  STA 701 Advanced Statistical Inference I  STA 703 Advanced Probability  STA 705 Advanced Computational Inference 
Spring, Year Three  STA 707 Advanced Data Analysis  STA 709 Advanced Survival Analysis  Elective 
Fall, Year Four  Elective  Residency  Residency 
Spring, Year Four  Elective  Residency  Residency 
The residency policy at the University of Kentucky is described in the Graduate School Bulletin.
Students must pass a uniform written exam over STA 701 and STA 703 plus respective prerequisites. This exam will normally be offered at the end of May in the third year of the program. The uniform exam can be repeated once. A student can choose their advisor at any time. After completion of tract course requirements and successful completion of the written exam, students must also successfully complete an oral qualifying exam which is scheduled in consultation with the student's advisory committee. A significant part of this exam is to be a dissertation proposal. For details, see the Graduate School Bulletin.
Recent PhD Graduates and Dissertation Abstracts
Below is a list of our recent graduates and the titles of their dissertations**. If you would like to view any of the abstracts for the dissertations listed below, please use this link.

2020
 Aisaku Nakamura  Simultaneous Tolerance Intervals for Response Surface and Mixture Designs Using the Adjusted Product Set Method

Aric Schadler  Measuring Change: Prediction of Early Onset Sepsis

Kedai Cheng  Tolerance Intervals for Time Series Models and Specifying Trimming/Winsorizing Cutoffs

Li Xu  Estimation of the Treatment Effect with Bayesian Adjustment for Covariates

Matthew Rutledge  Measuring Variability in Model Performance Measures
 Tingting Zhai  Cancer Phylogenetic Analysis Based on RNASEQ Data
 Weihang Ren  Moment Kernels for TCentral Subspace
 Xu Zhang  Bayesian Kinetic Modeling for TracerBased Metabolomic Data
 Xue Ding  Statistical Methods in Cancer Clinical Trials and Biomedical Research
 Yan Xu  Nonparametric Tests of Lack of Fit for Multivariate Data
 Yixuan Zou  Statistical Intervals for Various Distribution Methods Based on Different Inference Methods
 Yue Cui  Nonparametric Analysis of Clustered and Multivariate Data
 Yuntong Li  Semiparametric and Nonparametric Methods for Comparing Biomarker Levels between Groups

2019
 Alejandro Villasante Tezanos  Composite Nonparametric Tests in High Dimension
 Chenlu Ke  A New Independence Measure and Its Applications in High Dimensional Data Analysis
 Eric Roemmele  A Flexible ZeroInflated Poisson Regression Model
 Jiaying Weng  Transforms in Sufficient Dimension Reduction and their Applications in High Dimensional Data
 Qiwen Kang  Unsupervised Learning in Phylogenomic Analysis Over the Space of Phylogenetic Trees
 Zaid Al Khaledi  Serial Testing for Detection of Multilocus Genetic Interactions

2018
 Amanda Ellis  Accounting for Matching Uncertainty in Photographic Identification Studies of Wild Animals
 Ye Li  Multifactor Dimensionality Reduction With P Risk Scores Per Person

2017
 Qingcong Yuan  Informational Index and Its Applications in High Dimensional Data

2016
 Hong Wang  Improved Models for Differential Analysis for Genomic Data
 Hongyuan Wang  Statistical Inference on Dynamical Systems
 Kristen McQuerry  Statistical Methods for Handling Intentional Inaccurate Responders
 Lijie Wan  Continuous Time MultiState Models for Interval Censored Data
 Meng Qi  Development of Normal Mixture and Mixture of Experts Modeling
 Rebecca Crouch  Aggregated Quantitative Multifactor Dimensionality Reduction
 Shu Shen  Developing an Alternative Way to Analyze NanoString Data
 Yuchen Yang  Statistical Methods for Environmental Exposure Data Subject to Detection Limits
 Yumin Zhao  Statistical Inference on Trimmed Means, Lorenz Curves, and Partial Area Under ROC Curves by Empirical Likelihood Methods
 Zhiheng Xie  Topics in Logistic Regression Analysis
 Zhiyuan Shen  Empirical Likelihood and Differentiable Functionals

2015
 Edward Roualdes  New Results in ell_1 Penalized Regression
 Grady Weyenberg  Statistics in the BilleraHolmesVogtmann Treespace
 Shaoceng Wei  MultiState Models for Interval Censored Data with Competing Risk
 Shihong Zhu  Empirical Likelihood Confidence Band

2014
 Feng Zhou  Contaminated Chisquare Modeling and its Application in Microarray Data Analysis
 Qian Fan  Normal Mixture and Contaminated Model with Nuisance Parameter and Applications
 Yinglei Li  Genetic Association Testing of Copy Number Variation

2013
 Jing Xi  Polytopes Arising from Binary Multiway Contingency Tables and Characteristic Imsets for Bayesian Networks
 Limin Feng  JamesStein Type Compound Estimation of Multiple Mean Response Functions and Their Derivatives
 Xiang Zhang  Analysis of Spatial Data

2012
 Zilong Wang  Analysis of Binary Data via SpatialTemporal Autologistic Regression Models

2011
 Angela Schoergendorfer  Bayesian Semiparametric Generalizations of Linear Models using Polya Trees
 Yan Xie  Stochastic Dynamics of Gene Transcription
 Yanling Hu  Some Contributions to the Censored Empirical Likelihood with HazardType Constraints
 Yushun Lin  Parametric Estimation in Competing Risks and Multistate Models

2010
 Wenjie Lou  Multistate Models with Missing Covariates

2009
 Liping Huang  Statistical Methods in Microarray Data Analysis

2008
 Costel Chirila  Empirical Processes and ROC Curves with an Application to Linear Combinations of Diagnostic Tests
 Skyler Speakman  Phylogenetic Methods for Testing Significant Codivergence between Host Species and their Symbionts

2006
 Christopher Saunders  Empirical Processes for Estimated Projections of Multivariate Normal Vectors with Applications to E.D.F. and Correlation Type Goodness of Fit Tests
 Heather Bush  KhatriRao Products and Conditions for the Uniqueness of PARAFAC Solutions for IXJXK arrays
 Hua Liu  Asymptotic Properties of Partial Areas Under the Receiver Operating Characteristic Curve With Applications in Microarray Experiements
**The list above was generated from incomplete data. If you are unable to find a specific dissertation (or we accidentally missed one!) please email StatOffice@uky.edu and we will look into it as soon as we are able to. Thank you!