Generalized linear models are considered within the larger framework of multiparameter exponential family models. This general approach shows that the link function is not a necessary feature of a computer algorithm for calculating maximum likelihood estimates for such models by iteratively reweighted least squares. It is argued that the link function is a useful component of model fitting and interpretation in situations where there is a natural link to an underlying linear model (e. g. , logistic regression). However in many instances there is no single link function (e. g. , multinomial regression) or else unlinked parameters exist (e. g. , bioassay with a spontaneous response rate). It is shown by a number of examples that a general approach via exponential family models is preferable in such situations. 16 refs.
|Original language||English (US)|
|Number of pages||7|
|State||Published - Jan 1 1984|
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty