Iteratively Reweighted Least Squares for Models with a Linear Part

Abstract

Nelder and Wedderburn's method for maximum likelihood estimation of the parameters in an exponential family of regression models is extended to a more general type of model. Examples are given of the method's use for censored and grouped data, models involving the negative binomial or beta‐binomial distributions and in robust estimation. In a numerical example the algorithm converges considerably faster than the EM algorithm.