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 ...

In many applications, the response variable is not Normally distributed. GLM can be used to analyze data from various non-Normal distributions. In this short course, we will introduce two most common ...

This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of ...

Generalized linear mixed models (GLMM) are useful in a variety of applications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose ...

Cover -- Title Page -- Copyright Page -- Table of Contents -- Acknowledgments -- 1 Introduction and Background -- 1.1 Introduction -- 1.2 What This Book Is Not About ...