Generalized linear model: Difference between revisions

Revision as of 07:12, 3 August 2021

Revision as of 00:51, 9 August 2021

Line 26:

In this framework, the variance is typically a function, '''V''', of the mean:

:<math> \operatorname{Var}(\mathbf{Y}|\mathbf{X}) = \operatorname{V}( \boldsymbol{\mu} ) = \operatorname{V}(g^{-1}(\mathbf{X}\boldsymbol{\beta})). </math>

It is convenient if '''V''' follows from an exponential family of distributions, but it may simply be that the variance is a function of the predicted value.