Course: STAT 34700
Title: Generalized Linear Models
Instructor(s): Jingshu Wang
Teaching Assistant(s): Nathan Gill, Zehao Niu, Jason Willwerscheid
Class Schedule: Sec 01: TR 2:00 PM–3:20 PM in Eckhart 133
Textbook(s): Agresti, Foundations of Linear and Generalized Linear Models
Description: This applied statistics course is a successor of STAT 34300 and covers the foundations of generalized linear models (GLM). We will discuss the general linear modeling idea for exponential family data and introduce specifically models for binary, multinomial, count and categorical data, and the challenges in model fitting, and inference. We will also discuss approaches that supplement the classical GLM, including quasi-likelihood for over-dispersed data, robust estimation, and penalized GLM. The course also covers related topics including mixed effect models for clustered data, the Bayesian approach of GLM, and survival analysis. This course will make a balance between practical real data analysis with examples and a deeper understanding of the models with mathematical derivations.
Prerequisite(s): STAT 34300 or consent of instructor