## Notes And

### BibTeX

@MISC{Course_notesand,

author = {Extensions For Course and W. W. Piegorsch and Walter W. Piegorsch},

title = {Notes And},

year = {}

}

### OpenURL

### Abstract

ictor variable such as time or temperature. In its basic form, the classical linear model is unable to account for such effects. It is possible, however, to generalize the linear model, and allow it to overcome these limitations. The generalizations accept forms of non-normal data, and also link the mean response to the linear predictor in a possibly nonlinear fashion. We call this the family of generalized linear models (GLiMs). The basic precept of a GLiM is to extend the linear model in two ways: (a) generalize to non-normal parent distributions such as binomial, Poisson, or gamma; and/or (b) generalize to nonlinear functions that link the unknown means of the parent distribution with the predictor variables. In the former case, certain generalizations to non-normal parent distributions can be grouped into a class of densities known as the exponential family. The density functions satisfy two basic requirements: (1) the random variable, Y, has a support