num_pages: 479
description: Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian or even discrete response. GLM theory is predicated on the exponential family of distributions--a class so rich that it includes the commonly used logit probit and Poisson models. Although one can fit these models in Stata by using specialized commands (for example logit for logit models) fitting them as GLMs with Stata's glm command offers some advantages. For example model diagnostics may be calculated and interpreted similarly regardless of the assumed distribution.This text thoroughly covers GLMs both theoretically and computationally with an emphasis on Stata. The theory consists of showing how the various GLMs are special cases of the exponential family showing general properties of this family of distributions and showing the derivation of maximum likelihood (ML) estimators and standard errors. Hardin and Hilbe show how iteratively reweighted least squares another method of parameter estimation are a consequence of ML estimation using Fisher scoring.
language: English
title: Generalized Linear Models and Extensions
publisher: Stata Press