Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and direct the program.

This course covers the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software.Prerequisites: STAT 3120.

Studies experimental designs for randomized clinical trials, sources of bias in clinical studies, informed consent, logistics, and interim monitoring procedures (group sequential and Bayesian methods). Prerequisite: A basic statistics course (MATH 3120/5100) or instructor permission.

Research into current statistical problems under faculty supervision.

This course develops fundamental methodology to the analysis of longitudinal data. Topics include data structures, modeling the mean and covariance, estimation and inference with respect to the marginal models, linear mixed-effects models, and generalized linear mixed-effects models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 6120 and graduate standing in Statistics.

This course develops fundamental methodology to the analysis of categorical data. Topics include contingency tables, generalized linear models, logistic regression, and logit and loglinear models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.Prerequisite: Graduate standing in Statistics, or instructor permission.

Course provides an introduction to Bayesian methods with an emphasis on modeling and applications. Topics include the elicitation of prior distributions, deriving posterior and predictive distributions and their moments, Bayesian linear and generalized linear regression, and Bayesian hierarchical models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 6120, STAT 6190, and graduate standing in Statistics.

In this course, students will read, present, and discuss research papers on topics that are closed related to faculty's research interests, so that students have understandings of research profiles in the department and start to approach faculty members for thesis advising based on their interests developed in this topic course. This course helps the students to transition from course taking to thesis research. Topics will vary from term to term.

This course develops skills in reading the statistical research literature and prepares the student for contributing to it. Each student completes a well written and properly formatted paper that would be suitable for publication. The paper reviews literature relevant to a specialized research area, and possibly suggests an original research problem. Topics will vary from term to term.

This course introduces fundamental concepts in the classical theory of statistical inference. Topics include sufficiency and related statistical principles, elementary decision theory, point estimation, hypothesis testing, likelihood-ratio tests, interval estimation, large-sample analysis, and elementary modeling applications. Prerequisite: STAT 6190 and graduate standing in Statistics