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 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 ...

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 ...

Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K ...

Generally speaking, there are two types of outcomes (i.e. response) in statistical analysis: continuous and categorical responses. Linear Models (LM) are one of the most commonly used statistical ...

Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...