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 ...

This paper investigates new aspects of robust inference for general linear models, calling for a broader array of error measures, beyond the conventional notion of ...

Linear mixed models (LMMs) serve as a versatile statistical framework, combining fixed effects that capture the overall trends with random effects that account for variability across subjects, ...

Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

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