PhD Program Description
The degree of Doctor of Philosophy is a research degree, conferred in recognition of marked ability and scholarship and high scholastic attainment and original research in Statistics and Biostatistics. The degree is conferred after successful completion of an acceptable thesis summarizing substantial results of original research work relevant to Statistics and/or Biostatistics. Thesis work will be carried on with the general guidance and under the supervision of the candidate's Thesis Advisor.
Areas of specialization for research include any topic suitable for research in applied or theoretical statistics, including statistical inference, estimation theory, hypothesis testing, decision theory, empirical Bayes and Bayes methods, regression analysis, analysis of variance, statistical computing, experimental design, multivariate analysis, nonparametric statistics, sequential analysis, quality control theory, time series analysis, applied probability, stochastic processes, and probability theory.
Formal Credit Requirements
Ph.D. candidates must ordinarily have at least 72 semester-hours of approved graduate credits. This will generally consist of 48 hours of course credits and the remainder of the 24 hours as research credits. Ph.D. students are urged to spend at least one full academic year in residence on campus, although there is no formal residency requirement.
Transfer of Credits
Up to 30 credits of such acceptable credits may be permitted to be applied for the Ph.D. degree. This is subject to individual consideration.
A Guide for Courses and Electives for Ph.D. in Applied and Mathematical Statistics
587 Interpretation of Data II
592 Theory of Probability
593 Theory of Statistics
652-653 Advanced Theory of Statistics I and II
663 Regression Theory
680-681 Advanced Probability Theory I and II (or two other 600-level courses approved by the Graduate Director)
Two additional 600 level courses in Statistics
693 Current Topics in Statistics, 3 semesters
540-541 Quality Control I and II
542 Life Data Analysis
545 Statistical Practice
553 Categorical Data Analysis
554 Applied Stochastic Processes
555-655 Nonparametric Statistics & Advanced Nonparametric Statistics
563 Regression Analysis
565 Applied Time Series Analysis
567 Applied Multivariate Analysis
575 Acceptance Sampling Theory
576 Survey Sampling
584-585 Biostatistics I and II
586 Interpretation of Data I
588 Data Mining
590-591 Design of Experiments & Advanced Design of Experiments
595 Intermediate Probability
654 Stochastic Processes
664 Advanced Topics in Regression and Analysis of Variance
667 Multivariate Analysis
687-688 Seminar in Applied and Mathematical Statistics
689 Sequential Methods
690-691 Special Topics (topics on rotating basis): Large Sample Theory, Time Series
Bayesian Statistics, Robustness, Sequential Analysis.
A precondition to being formally admitted as a Ph.D. candidate is that the student pass the Ph.D. Examinations, the purpose of which is to determine the breadth of the student's mastery of their major and minor fields. The first of these exams is a comprehensive written qualifying exam on all first-year course material. The qualifying exam is taken before the second-year and consists of three parts: one exam each on mathematical statistics, probability theory, and applied statistics. Progress toward the Ph.D. is monitored throughout the second and third years, culminating in a written thesis proposal (due by the end of the 2nd semester of the third year) and an oral thesis proposal (presented no later than the 1st semester of the fourth year).