||Generalized Linear Models
||Nathan Gill and Yuxin Zou
||Sec 01: TR 2:00 PM–3:20 PM in Pick 016
||Faraway, Extending the Linear Model with R, 2nd edition.
|| 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