Program Description: Program Description: Doctor of Philosophy

The Ph.D. course requirements are sufficiently flexible to allow the student to pursue either a methodological or theory oriented plan of study. The student, in consultation with his/her adviser and Ph.D. advisory committee, can design a course of study to adequately prepare for his/her chosen dissertation topic.


a. STAT 720, STAT 730
b. Three additional statistics lecture courses at the 700 level. (STAT 721, 722, 725, 731, 740, 750, 760, 770)
c) STAT 799 (Dissertation)
(Students may take any of the courses STAT 721, STAT 722, ST 731, and/or STAT 740 twice provided there is no overlap in course content. However, such course(s) may only be counted once in meeting the Ph.D. credit requirement of the Graduate School. Students are expected to register for STAT 792, Seminar, each semester.)


The student is expected to meet with his/her Ph.D. advisory committee for the purpose of selecting the remainder of his/her course work. This meeting should take place prior to the completion of the student's program of study (Graduate School Form GS-6). Written approval of the student's program of study by the advisory committee is required. Every student is expected to take at least one lecture course each semester. (The University requires a total of 72 credits for the Ph.D. degree, of which a maximum of 30 credits may be carried over from the master's degree.)



Students are admitted into the M.S./Ph.D. program upon acceptance to the Statistics graduate
program. A student will be eligible to begin preparations to undertake research for a Ph.D. thesis
and to choose a Ph.D. thesis advisor once her/his Candidacy Portfolio has been evaluated as Pass by
the Candidacy Review Committee.

A Candidacy Portfolio consists of performance in the Candidacy Courses:
• STAT 520
• STAT 530
• STAT 540
• STAT 640

Annually, the Candidacy Review Committee will review Candidacy Portfolios for all students in May
and for second year students in January.

For each student, the Candidacy Review Committee will review the instructor recommendation for each
Candidacy Course and make an evaluation of Pass, Insufficient, or Fail for the Candidacy Portfolio.
In the case of an Insufficient rating, the Candidacy Review Committee will specify a requirement
for earning a Pass to replace the Insufficient rating. The default requirement is to retake any
course in which an Insufficient rating has been given. An alternate requirement may be specified by
the Candidacy Review Committee, in which case the Committee will specify a time frame and a
standard for earning a Pass in the specified requirement.

The Candidacy Review Committee will evaluate the Candidacy Portfolios for students that have
evaluations for all Candidacy Courses and any additional requirements, and then issue an evaluation
of Pass or Fail for the Portfolio. Earning a Pass for the Candidacy Portfolio satisfies the
Doctoral Candidacy Requirement.

Students must pass the Doctoral Candidacy Requirement within two years beginning from the date of
first enrollment in the Graduate Program.

Evaluation of Transfer Students and Requests for Prior Credit

Students may request evaluation of equivalent graduate courses taken at other institutions as
substitutions for one or more of the Candidacy Courses. Such requests must be accompanied by the
syllabus or syllabi of the relevant courses, the instructor(s), and the grade(s) earned. The
Candidacy Review Committee will evaluate each request and make an evaluation of Pass, Conditional,
or Not Accepted. In the case of Conditional, the Candidacy Review Committee will specify a course
in a related subject but at higher level that must be passed with the Instructor recommendation of
Pass. In the case of Not Accepted, the student will take the Candidacy Courses in question.

An Instructor of a course being used to meet an evaluation of Conditional will meet with the
Candidacy Review Committee and make a recommendation.

Candidacy Review Committee

The Candidacy Review Committee will consist of all regular faculty.


The Ph.D. preliminary examination is an oral exam administered by the student's graduate advisory committee after having passed STAT 720 and STAT 730. The Graduate School requires that this examination be administered at least two terms before the final dissertation defense.


A satisfactory dissertation must be completed, approved by the student's graduate advisory committee, and defended in a final oral examination which is open to the University community. The dissertation must constitute original research in statistical theory or applications and must be submitted in a form acceptable to the Graduate School. The student's dissertation results must be presented in a Departmental seminar.


STAT 720: Probability Theory. 04 (4-0-0) F

Prerequisite: ST 520, M 517

Measure theoretic probability, characteristic functions; convergence; laws of large numbers; central limit, extreme value, asymptotic theory.

STAT 721: Applied Probability and Stochastic Processes I 03 (3-0-0) F, S

Prerequisite: ST 720

General theory of processes; Markov processes in discrete and continuous time; review of martingales, random walk; renewal and regenerative processes; stationary processes.

STAT 722: Applied Probability and Stochastic Processes II 03 (3-0-0) F, S, SS

Prerequisite: ST 721

Brownian motion, diffusion, stochastic differential equations; weak convergence and central limit theorems. Applications in engineering, natural sciences.

STAT 725: Time Series and Stationary Processes. 03 (3-0-0) F, S, SS

Prerequisite: ST 720, ST 730

Spectral theory of multivariate stationary processes; estimation, testing for spectral, linear, ARMA representations; best linear predictors, filters.

STAT 730: Advanced Theory of Statistics I 04 (4-0-0) F

Prerequisite: ST 530, ST 720

Minimal sufficiency, maximal invariance; Neyman-Pearson theory; Fisher, Kullback-Leibler information; asymptotic properties of maximum-likelihood methods.

STAT 731: Advanced Theory of Statistics II 03 (3-0-0) S, SS

Prerequisite: ST 730

Decision-theory model; Bayes, e-Bayes, complete, and admissible classes; applications to sequential analysis and design of experiments.

STAT 740: Advanced Statistical Methods 03 (3-0-0) F, S, SS

Prerequisite: ST 721

Generalized additive models; recursive partitioning regression and classification; graphical models and belief networks, spatial statistics.

STAT 750: Advanced Theory of Design. 03 (3-0-0) F, S

Prerequisite: ST 650 or written consent of instructor

Information theory; design evaluation, factorial designs and optimal designs, orthogonal and balanced arrays, designs with discrete/continuous factors.

STAT 760: Theory of Multivariate Statistics 03 (3-0-0) F, SS

Prerequisite: ST 640, concurrent registration in ST 730

Theory of multivariate normal; maximum-likelihood inference, union-intersection testing for single sample; theory of a multivariate linear model.

STAT 770: Approximation Theory and Methods 03 (3-0-0) F, S, SS

Prerequisite:ST 720, ST 730

Edgeworth expansions, saddlepoint methods; applications of weak convergence and other approximation methods in mathematical statistics.

STAT 792: Seminar 01 (0-0-1) F, S


STAT 795: Independent Study Variable Credit

Independent Study

STAT 795: Independent Study Variable Credit

Independent Study

STAT 796: Group Study Variable Credit

Methodology, stochastic processes, experimental design, multidimensional statistics.

STAT 799: Dissertation Variable Credit