Introduction to Generalized Linear Models

Authors: Alan Agresti - Wiley series in probability and statistics 2002 cited by 358

Chapter 4 presents an introduction to generalized linear models. It introduces the most important models for binary data (logistic regression) and for counts (Poisson regression). It also gives a theoretical presentation of derivation of likelihood equations and methods of maximum likelihood model-fitting that apply for all models in this class. The chapter also presents extensions such as quasi likelihood and generalized additive models.