For doctoral research, taken under the supervision of a dissertation director.

Advanced graduate seminar in current research topics. Offerings in each semester are determined by student and faculty research interests.

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.

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.

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 introduces fundamental concepts in probability that underlie statistical thinking and methodology. Topics include the probability framework, canonical probability distributions, transformations, expectation, moments and momentgenerating functions, parametric families, elementary inequalities, multivariate distributions, and convergence concepts for sequences of random variables.Prerequisite:Graduate standing in Statistics, or instructor permission.

Course develops fundamental methodology to regression and linear-models analysis in general. Topics include model fitting and inference, partial and sequential testing, variable selection, transformations, diagnostics for influential observations, multicollinearity, and regression in nonstandard settings. Conceptual discussion in lectures is supplemented withhands-on practice in applied data-analysis tasks using SAS or R statistical software.Prerequisite: Graduate standing in Statistics, or instructor permission.

Research into current statistical problems under faculty supervision.

"Topics include importing data from various sources into R/SAS, manipulating and combining datasets, transform variables, "clean" data so that it is ready for further analysis, manipulating character strings, export datasets, and produce basic graphical and tabular summaries of data. More advanced topics will include how to write, de-bug, and tune functions & macros. Approx. equal time will be spent using SAS and R. Prereq: Intro statistics course"

This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisites: Previous or concurrent enrollment in STAT 5120 or STAT 6120.