Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 40, No. 4, Special Issue in Honour of Mary Thompson (December/décembre 2012), pp. 745-769 (25 pages) In this paper, we ...

A simple heuristic is proposed for constructing robust experimental designs for multivariate generalized linear models. The method is based on clustering a set of local optimal designs. A method for ...

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...