Generalized linear model

Summary

A generalized linear model relates the mean of a response to a linear combination of regressors through a link function. The response distribution is taken from an exponential family. It unifies linear regression, logistic regression, Poisson regression and the probit model in one framework, with one fitting method and one theory of inference. The framework was introduced by John Nelder and Robert Wedderburn in 1972. Its coefficients are estimated by maximum likelihood, usually with iteratively reweighted least squares.