Generalized Linear Models

Part of the Statistics and Computing book series (SCO)


Generalized linear models (GLMs) extend linear models to accommodate both non-normal response distributions and transformations to linearity. (We will assume that Chapter 6 has been read before this chapter.) The essay by Firth (1991) gives a good introduction to GLMs; the comprehensive reference is McCullagh & Neider (1989).


Generalize Linear Model Linear Predictor Negative Binomial Model Residual Deviance Stimulus Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of AdelaideAdelaideAustralia
  2. 2.University of OxfordOxfordEngland

Personalised recommendations