When applying the generalized linear model or GLM to the real world, a phenomenon called overdispersion occurs when the observed variance of the data is larger than the predicted variance. This is particularly apparent in the case of a Poisson regression model, where
predicted variance = predicted mean,
or the binary logistic regression model, where
predicted variance = predicted mean(1- predicted mean).
A parameter, called the dispersion parameter, , is introducted to the model to lower this overdispersion effect.
The GLM, with the inclusion of this dispersion parameter, has the following density function:
|Date of creation||2013-03-22 14:30:34|
|Last modified on||2013-03-22 14:30:34|
|Last modified by||CWoo (3771)|