CAS MA 576: Generalized Linear Models (Spring 2009)

Instructor: Dr. Surajit Ray
Department of Mathematics and Statistics
MCS 222, 111 Cummington Street, Boston, MA 02215
Phone: (617) 353-5209, Fax: (617) 353-8100
Class Time: Tue,Thu 9:30am 11:00am
Class Room: MCS 148
Office Hours: TBD

Blackboard Login ( BU Students)

Course Description:
Covers topics in linear models beyond MA 575: generalized linear models, analysis of binary and polytomous data, log-linear models, multivariate response models, non-linear models, graphical models and relevant model selection techniques. Additional topics in modern regression as time allows.

Homework 30%, Midterms 30%, Final 30%, Class participication and discussions 10%.
Biweekly homeworks and 2 Midterms.

Week-by-Week Syllabus:

Week Topic
1 Summary of Topics covered in MA 575: Linear models.
2 Outline of Generalized Linear Models (GLM).
3 Non-normal distribution and Link functions.
4 Models for Continuous Data with Constant Variance.
5 Estimation in GLM: MLE, Extension of Least Square estimation.
6 Models for Binary Data Estimation and Deviance measures.
7 Models for Polytomous Data.
8 Log-linear Models.
9 Conditional likelihood and Quasi Likelihood functions.
10 Conditional likelihood and Quasi Likelihood functions.
11 Models with non-linear parameters.
12 Model Checking and Model Selection for GLM's.
13 Modeling the Variance: Structural Covariance, Structural Equation Model and Multivariate response models.
14 Introduction to Robust Regression and Principal Component Regression.

P. McCullagh; John A. Nelder Generalized Linear Models, Second Edition Chapman and Hall

List Price: $104.95
Cat. #: C1760
ISBN: 0412317605
Publication Date: 8/1/1989
Number of Pages: 532
Availability: In Stock

Supplementary Book
Charles E. McCulloch, Shayle R. Searle Generalized, Linear, and Mixed Models Wiley, New York

Generalized, Linear, and Mixed Models [E-Book]
Charles E. McCulloch, Shayle R. Searle
ISBN: 0-471-65404-3
April 2004
US $120.00 Purchase This E-Book
We will use the statistical software R (or its commercial predecessor, S-Plus) for computing in this course. The software is now becoming a standard for users both in
and outside of statistics, particularly those that need to do programming of new methods. The software is free and compatible with Windows, Mac, and Linux/Unix. It may be downloaded from The main website for the R project is For those that do not wish to install R on their own machines, it can be accessed in a command-line version on the ACS machines and, for those students in the department, on the /machines in the Department of Mathematics and Statistics.

It is expected that all students will be responsible for getting up to speed on the
basics of the R package on their own within the second week or so.  There are some excellent resources on the web e.g the official manuals and the R Primer . During the course of the semester I will list more resources.

Please Note:
You are responsible for knowing, and abiding by, the provisions of the GRS Academic Conduct Code, which is posted at
Violations of the code are punishable by sanctions including expulsion from the University.