Fall 2009 STAT 700A: GLM

Instructor: Anindya Roy    Office: MP 435 phone: 410-455-2435
                   email: anindya@math.umbc.edu
Office Hours: MW 3:00-4:00 pm, or by appointment Class meets::   W 7:10-9:40 in PHYS 107
Textbook: Faraway: Extending the Linear Model with R
Course prerequisites: Knowledge of Multiple Linear Regression and Statistical Inference
Grading Homework 60% Final Project 40%
Course Description and Learning Goals The Course will cover estimation and inference in Generalized Linear Models (GLM); special cases such as logistic regression will be covered in more detail. Generalized Estimating Equations (GEE) and othr related estimation methods will be discussed; Estimation and hypothesis testing for fixed effects and variance components in Generalized linear mixed models (GLMM) will be discussed. Objectives are to get a clear understanding of estimation and inferential procedures in GLM and to be able implement the procedures using statistical software for common GLM problems.
Additional reading:

McCulloch, C. E. and Searle, S. A. (2001) Generalized, Linear and Mixed Models

Dobson, A. J. (1990) An Introduction to Generalized Linear Models

Hosmer, D. W. and Lemeshow, S. (1989) Applied Logistic Regression Analysis

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models

Academic Honesty: By enrolling in this course, each student assumes the responsibility of an active participant in UMBC's scholarly community in which everyone's academic work and behavior are help to the highest standards of honesty. Cheating, fabrication, plagiarism, and helping others to commit these acts are all forms of academic dishonesty, and they are wrong. Academic misconduct could result in disciplinary action that may include, but is not limited to, suspension or dismissal. To read the full Student Academic Conduct Policy, consult the UMBC student handbook, the Faculty Handbook or the UMBC Policies section of the UMBC directory.