ABSTRACT
The term “generalized linear model” (GLM) was first introduced in a landmark paper by Nelder and Wedderburn (1972), in which a wide range of seemingly disparate problems of statistical modeling and inference were set in an elegant unifying framework of great power and flexibility. Generalized linear models include all the modeling techniques described in earlier chapter, that is analysis of variance, analysis of covariance, multiple linear regression, and logistic regression, and open up the possibility of other models, for example,
Poisson regression
, that we shall describe in this chapter. A comprehensive account of GLMs is given in McCullagh and Nelder (1989), and a more concise description in Dobson (2001). In the next section we review the main features of such models.
