Generalized Linear Models

Welcome to AMS274 : Generalized Linear Models  


General Class Information

Instructor: Tatiana Xifara

Office: Baskin Engineering 365B 
Office Hours: Tuesday 2:30-4:00pm, Wednesday 1:30-3:00pm, or by appointment

Lectures:Tuesday, Thursday 12:00- 1:45pm, Porter Acad 241

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 is given in the syllabus.

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.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. 

Course Syllabus

Notes on GLM definitions, and maximum likelihood estimation for GLMs

References on Bayesian approaches to modeling and inference with GLMs



Homework 1 (Due to Thursday April 10)

Homework 2 (Due to Thursday April 24)

Homework 3 (Due to Thursday May 8th)

Homework 4 (Due to Friday May 30th)  

Homework 5 (Due to Wednesday June 11 12pm)


Instructors and Assistants