|Title:||Generalized Linear Models|
|Teaching Assistant(s):||Nathan Gill and Yuxin Zou|
|Class Schedule:||Sec 01: TR 2:00 PM–3:20 PM in Pick 016|
|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