Lecture Times: Tuesday, Thursday 10:00- 11:45am, BE 358
Instructor: Tatiana Xifara
Email: xifara@soe.ucsc.edu
Office: Baskin Engineering 365B
Office Hours: Monday 2:00-3:30pm or by appointment.
Required Text: The course will not use a textbook. Good reference textbooks include "Generalised Linear Models" by McCullagh & Nelder, "Categorical Data Analysis" by Agresti and "Generalised Linear Models: A Bayesian Perspective" by Dey, Chosh and Mallick. An extended list of references will be given in class.
Course Objectives: This is a graduate-level course on basic theory, methodology and applications of Generalized Linear Models (GLMs). Emphasis will be placed on statistical modeling, building from standard normal linear models, extending to GLMs, and going beyond GLMs. The course will cover both frequentist inference and Bayesian methodology approaches but the focus will be Bayesian methodology. The course will contain an introductory overview of Bayesian methods for analysis of binomial, count, categorical, and event time data. In particular, within the Bayesian modeling framework, we will discuss particularly important hierarchical extensions of the standard GLM setting. We will be using the statistical software R to illustrate the methods with examples and case studies.
Tentative Schedule: We will cover topics from the following:
1. Introduction to GLMs
2. Likelihood inference for GLMs
3. Bayesian inference in GLMs (basics)
4. Regression models for categorical responses and count data
5. Bayesian inference in GLMs (continued)