| Course: | STAT 34700 |
| Title: | Generalized Linear Models |
| Instructor(s): | Yali Amit |
| Teaching Assistant(s): | Nathan Gill and Yuxin Zou |
| Class Schedule: | Sec 01: TR 2:00 PM–3:20 PM in Pick 016 |
| Office Hours: | |
| Textbook(s): | Faraway, Extending the Linear Model with R, 2nd edition. |
| Description: | This course covers exponential-family models; definition of generalized linear models; specific examples of GLMs; logistic and probit regression; cumulative logistic models; log-linear models and contingency tables; Quasi-likelihood and least squares; estimating functions; survival analysis; linear mixed models and generalized linear mixed models; and derivation of the methods are presented including likelihood analysis and some basic asymptotic properties. The course emphasizes the use and interpretation of generalized linear models with the R package. Techniques discussed are illustrated by examples involving physical, biological, and social science data. Prerequisite(s): STAT 34300 or consent of instructor |