Doctor of Philosophy Degree

The information contained on this website is for informational purposes only. The Undergraduate Record and Graduate Record represent the official repository for academic program requirements. These publications may be found at http://records.ureg.virginia.edu/index.php

Programs of Study

The Department of Statistics administers programs leading to the degrees of Master of Science (M.S.) and Doctor of Philosophy (Ph.D.). These programs provide diverse opportunities for advanced study and research in all areas of applied and theoretical statistics, and practical experience in statistical consulting

The Ph.D. program requires 47 hours of graded coursework, which includes all M.S.-level coursework and additional advanced theory and methodology courses, as well as courses that prepare the student for research. The Ph.D. student must also pass a series of examinations that assess his or her ability to do research, culminating in the Ph.D. Final Exam, for which the student submits and presents a written dissertation prepared under the supervision of an advisor and committee of Statistics faculty members. The Ph.D. program is normally completed within five years. 

Overview of Requirements

The successful completion of the following courses and examinations are required for the Ph.D. in Statistics, and are described more fully below.

Graded coursework requirements:

  • All in a list of lower-level required courses (15 credits)
  • All in a list of advanced-level required courses (8 credits)
  • At least four core elective courses (12 or more credits)
  • Both of two advanced core elective courses. (6 credits)
  • At least two free elective courses (6 or more credits)

Pre-candidacy examination requirements:

  • Computer Language Exam
  • Ph.D. General Exam (in two parts:  Theory and Applied Research).

Advanced examination requirements:

  • Ph.D. Preliminary Exam
  • Ph.D. Final Exam

Sample Ph.D. programs 

Milestones

The student becomes an “emerging candidate” for the Ph.D. upon passing the Computer Language Exam and both the Theory and Applied Research portions of the Ph.D. General Exam.

The student becomes a “candidate” (or, informally, “full candidate”) for the Ph.D. upon completing all graded coursework requirements and passing the Ph.D. Preliminary Exam.

The Ph.D. student must successfully complete all pre-candidacy examinations and thus have reached the milestone of “emerging candidate” by the conclusion of the third year of study in order to proceed to the advanced examinations. He or she is also to have an Advisory Committee of four members, part of whose role is to set the requirements of each exam.

Preparation

Students should have 3 semesters of calculus, linear algebra (comparable to UVa’s MATH 3351 or APMA 3080), and an introductory calculus-based probability and statistics course sequence, comparable to MATH 3100/STAT 3120 or APMA 3100/3120. Most students find it extremely helpful to have an introductory real analysis course (‘epsilon-delta proofs’) comparable to MATH 3310.

Course Requirements

The Ph.D. program requires 72 credit hours of coursework. Among these, the Ph.D. student must take 47 credit hours of graded coursework stipulated as follows.

All of the following lower-level required courses: STAT 5430, STAT 6120, STAT 6190, STAT 7100, and STAT 7995.

All of the following advanced-level required courses: CS 5014, STAT 6510, STAT 6520, and STAT 7200.

At least four lower-level core elective courses from the following list: STAT 6130, STAT 6160, STAT 6170, STAT 6250, STAT 6260, STAT 6440, STAT 7180, and STAT 7130.

Both of two advanced-level core elective courses: STAT 7510 and STAT 7520.

At least two free elective courses from the following list: STAT 5310, STAT 5140, STAT 5330, STAT 5265, STAT 5266, STAT 5340, STAT 5630, and STAT 8320.

Other graduate-level, three-credit courses from statistics or another department may substitute as a free elective course, subject to approval by the Director of Graduate Studies. Duplications are not allowed between 5000- and 6000-level versions of the same topic.

The entering Ph.D. student who wishes to strengthen his or her mathematical preparation may count MATH 5310 (Real analysis) as a free elective course, provided that it is taken in the student’s first year.

STAT 7510 and STAT 7520 are topics courses, which may be repeated for credit, provided the course syllabus substantially changes, as determined by the Director of Graduate Studies. A Ph.D. student who meets the advanced core elective course requirement may count any additional instance of STAT 7510 and STAT 7520 as a regular core elective course. An M.S. student may count an advanced core elective course as a regular core elective course.

Biostatistics Concentration

The Ph.D. student who wishes to complete the biostatistics concentration must take STAT 5140 plus at least one of STAT 5310 or STAT 8320. PHS 7950 may be substituted for either of STAT 5310 or STAT 8320.

Pre-Candidacy Examination Schedule

Computer Language Exam: The purpose of this exam is to assess whether the student is prepared for hands-on, data-intensive study of statistical methodology and applications, especially regarding the use of statistical software. The content is elementary data set management and elementary statistical analysis applications using SAS and R. The student is presented with a problem whose solution requires substantial manipulation of a large or complicated data set. This is a written exam whose format is timed and open-book; the student’s solution would consist of written comments, computer code, and possibly printouts of graphics and statistical output. The exam is offered at least once per year, near the end of the Fall or Spring semester, and is typically coordinated with STAT 5410.

The Ph.D. student must pass the Computer Language Exam before the start of his or her third year. A student who fails the Computer Language Exam may sit for the examination again at the discretion of the faulty.

Ph.D. General Exam, Theory: The purpose of the Theory portion of the Ph.D. General Exam is to assess whether the student is prepared to carry out statistical research that involves a substantial theoretical component. The content of the exam matches topics in probability and statistics that are typically covered in STAT 6190 and STAT 7100. The format is a timed, closed-book, written exam, to which the student is allowed to bring a formula sheet. It is offered at the beginning of each Fall semester.

The Ph.D. student must pass the Theory portion of the Ph.D. General Exam before the start of his or her third year. He or she may take this portion of the exam at most twice.

Ph.D. General Exam, Applied Research: The purpose of the Applied Research portion of the Ph.D. General Exam is to assess whether the student is prepared to carry out statistical research in two aspects: (i.) Is the student suitably prepared to work with data and apply statistical methodology toward a specific research goal? (ii.) Is the student suitably prepared to report his or her research contributions in publication-quality document? The student is to submit a paper that describes an interesting data set and related specialized area of research. The paper reviews literature and discusses, or at least suggests, an original research problem. The paper is to be well written and properly formatted in the manner of a publication in the statistics literature. This portion of the exam is intended to coordinate with the two-course sequence STAT 6510-6520.

The Ph.D. student must pass the Applied Research portion of the Ph.D. General Exam before the start of his or her fourth year. He or she may take this portion of the exam at most twice.

Advanced Examination Schedule

Ph.D. Preliminary Exam: The purpose of this exam is to assess whether the candidate has identified a research topic and plan for its development that is likely to result in a successful Ph.D. Final Exam. The format of the Ph.D. Preliminary Exam is a private, two-hour oral exam, presented to the candidate’s Advisory Committee. The traditional requirement is that the candidate is to either defend a dissertation prospectus, or give a talk about existing developments in his or her research area, and then submit to questioning afterward.

The Ph.D. student must pass the Ph.D. Preliminary Exam before the start of his or her fifth year. He or she may take this portion of the exam at most twice.

Ph.D. Final Exam: The purpose of this exam is to assess whether the candidate has been successful as a researcher, scholar, and an academic. The criteria to be considered are as follows: (i.) Has the candidate made an original research contribution? (ii.) Has the candidate produced scholarship at a level consistent with the standards of the statistics discipline? (iii.) Has the candidate achieved skills with which to engage in academic discussion? The Ph.D. Final Exam consists of two components, a written dissertation and a dissertation defense. The dissertation defense is in the format of a three-hour oral exam, consisting of a public portion (of at least one hour in length) and a private portion, presented to the Advisory Committee. The requirement is almost always that the candidate is to present the contributions of the written dissertation, and then submit to questioning afterward.

A candidate who fails the Ph.D. Final Exam cannot retake the exam, and is dismissed from the program

Course Descriptions

STAT 5000 - Introduction to Applied Statistics Credits: 3
STAT 5120 - Applied Linear Models Credits: 3
STAT 5140 - Survival Analysis and Reliability Theory Credits: 3
STAT 5150 - Actuarial Statistics Credits: 3
STAT 5170 - Applied Time Series Credits: 3
STAT 5180 - Design and Analysis of Sample Surveys Credits: 3
STAT 5310 - Clinical Trials Methodology Credits: 3
STAT 5330 - Data Mining Credits: 3
STAT 5410 - Introduction to Statistical Software Credits: 1
STAT 5430 - Statistical Computing with SAS and R Credits: 3
STAT 5510 - Contemporary Topics in Statistics Credits: 1
STAT 5630 - Statistical Machine Learning Credits: 3
STAT 5980 - Applied Statistics Laboratory Credits: 1
STAT 6120 - Linear Models Credits: 3
STAT 6130 - Applied Multivariate Statistics Credits: 3
STAT 6160 - Experimental Design Credits: 3
STAT 6190 - Intermediate Probability Credits: 3
STAT 6250 - Longitudinal Data Analysis Credits: 3
STAT 6260 - Categorical Data Analysis Credits: 3
STAT 6440 - Bayesian Analysis Credits: 3
STAT 7100 - Introduction to Advanced Statistical Inference Credits: 3
STAT 7130 - Generalized Linear Models Credits: 3
STAT 7150 - Non-Parametric Statistical Analysis Credits: 3
STAT 7180 - Sample Surveys Credits: 3
STAT 7200 - Advanced Probability Theory for Applied Scientists Credits: 3
STAT 7510 - Advanced Topics in Statistical Inference Credits: 3
STAT 7520 - Advanced Topics in Probability Credits: 3
STAT 7995 - Statistical Consulting Credits: 3
STAT 8120 - Topics in Statistics Credits: 3
STAT 8170 - Advanced Time Series Credits: 3
STAT 9120 - Statistics Seminar Credits: 3
STAT 9993 - Directed Reading Credits: 1 to 9
STAT 9998 - Non-Topical Research, Preparation for Doctoral Research Credits: 1 to 12
STAT 9999 - Non-Topical Research Credits: 1 to 12

The Statistics Colloquium

The colloquium is held weekly, with the sessions devoted to research activities of students and faculty members, and to lectures by visiting statisticians on current research interests.