Program Descriptions

Master of Science Degree Requirements

Shared Core

  • STA 602 (4) Introduction to Statistical Methods
  • STA 603 (4) Introduction to Linear Models and Experimental Design
  • STA 605 (3) Computational Inference
  • STA 606 (3) Theory of Statistical Inference I
  • STA 623 (3) Theory of Probability
  • STA 632 (3) Longitudinal Data Analysis

Mathematical Statistics Track

Curriculum requirements for the Mathematical Statistics track are the shared core courses above, plus the following courses:

  • STA 607 (3) Theory of Statistical Inference II
  • STA 624 (3) Applied Stochastic Processes
  • STA 643 (3) Advanced Experimental Design

Biostatistics Track

Curriculum requirements in the Biostatistics track are the shared core courses above, plus:

  • STA 635 (3) Survivability and Life Testing
  • STA 653 (3) Clinical Trials
  • STA 665 (3) Analysis of Categorical Data
  • STA 693 (2) Biostatistical Practicum, 1 unit course in each of the two semesters in the second year

Programs of study for Plan B require a total of at least 35 semester hours. Students will typically fulfill this requirement by taking electives (additional courses besides the shared core and track requirements) in Fall and Spring of their second year. Programs of study for Plan A (with thesis) require a total of at least 29 semester hours which are satisfied by either of the two course lists above.

The electives can be selected from the menu of courses listed below.

Before the end of the second semester, the M.S. candidate must present a proposed plan of study for approval by the Director of Graduate Studies. There are no formal minor requirements.

Comprehensive Examination

All master's candidates are required to pass a comprehensive departmental written examination on the content of the courses STA 602, STA 603, STA 605, STA 606, and STA 623. This examination is normally administered in late May/early June. It is truly comprehensive also in the sense that all parts must be taken together: If a student decides not to take a part of the examination, that part is automatically counted as failed. Students taking the comprehensive exam will receive either a pass at the doctoral level, a pass at the master's level, or a failure. The examination may be repeated only once.

Successful completion of the comprehensive examination at the doctoral level is required for admission into the PhD program.

Electives

The electives may be chosen from any course in the following menu that is NOT a track requirement.

  • MA 471G (3) Advanced Calculus I
  • STA 607 (3) Theory of Statistical Inference II
  • STA 612 (3) Sequential Analysis
  • STA 616 (3) Design and Analysis of Sample Surveys
  • STA 621 (3) Nonparametric Inference
  • STA 624 (3) Applied Stochastic Processes
  • STA 626 (3) Time Series Analysis
  • STA 630 (3) Bayesian Inference
  • CPH 631 (3) Design and Analysis of Health Surveys
  • STA 635 (3) Survivability and Life Testing
  • CPH 636 (3) Data Mining in Public Health
  • STA 643 (3) Advanced Experimental Design
  • STA 644 (3) Advanced Linear and Nonlinear Models
  • STA 653 (3)* Clinical Trials
  • STA 661 (3) Multivariate Analysis I
  • STA 662 (3) Resampling and Related Methods
  • CPH 664 (3)* Design and Analysis of Clinical Trials
  • STA 665 (3) Analysis of Categorical Data

Any course on this list NOT required for the chosen track may be used as an elective. Thus, for example, STA 665 would count as an elective for the Mathematical Statistics track, but it is a track requirement for the Biostatistics track. Similarly, STA 624 would be an elective for the Biostatistics track but is a track requirement for the Mathematical Statistics track.

* A student who takes both STA 653 and CPH 664, may only receive credit towards the degree for one of these two courses.

Recommended Schedules

In the following schedules, the electives can be anything from the elective list, including courses required for the respective other track. If you are planning on continuing for a PhD:

  1. Take STA607 and STA643 in Fall year 2, either as track requirements or electives.
  2. If you haven’t yet taken MA471G before, it should be taken in Fall year 2.
  3. Take STA700 in Spring year 2.

Mathematical Statistics Track

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 Elective
Spring, Year Two  STA 632 Longitudinal Data Analysis Elective Elective

 

Biostatistics Track

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 635 Survivability and Life Testing Elective

Elective

 STA 693 Biostatistical Practicum
Spring, Year Two  STA 632 Longitudinal Data Analysis STA 665 Analysis of Categorical Data Elective STA 693 Biostatistical Practicum

 

 



 

Doctoral Program Requirements

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 Advanced Calculus I
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 643 Advanced Experimental Design MA 471G Advanced Calculus I
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.

  • 2021
    • Xuan Shi - Novel Methods for Characterizing Conditional Quantiles in Zero-Inflated Count Regression Models
    • Pei Wang - Dimension Reduction Techniques in Regression
    • Jing Wei - Innovative Statistical Models in Cancer Immunotherapy Trial Design
    • Zi Ye - Estimating and Testing Treatment Effects with Misclassified Multivariate Data
    • Ting Zeng - Novel Nonparametric Testing Approaches for Multivariate Growth Curve Data: Finite-Sample, Resampling and Rank-Based Methods
  • 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 RNA-SEQ Data
    • Weihang Ren - Moment Kernels for T-Central Subspace
    • Xu Zhang - Bayesian Kinetic Modeling for Tracer-Based 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 Zero-Inflated 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
    • Xiaoli Kong -Thesis: High Dimensional Multivariate Inference Under General Conditions
  • 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 Multi-State 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 Billera-Holmes-Vogtmann Treespace
    • Shaoceng Wei - Multi-State Models for Interval Censored Data with Competing Risk
    • Shihong Zhu - Empirical Likelihood Confidence Band
  • 2014
    • Feng Zhou - Contaminated Chi-square 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 Multi-way Contingency Tables and Characteristic Imsets for Bayesian Networks
    • Limin Feng - James-Stein 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 Spatial-Temporal 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 Hazard-Type Constraints
    • Yushun Lin - Parametric Estimation in Competing Risks and Multi-state Models
  • 2010
    • Wenjie Lou - Multi-state 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
  • 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 - Khatri-Rao 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!