Generalized linear mixed models

'Generalized linear mixed models: modern concepts, methods and applications' presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.
Along with describing common applications of GLMMs, the text introduces the essential theory and main methodology associated with linear models that accommodate random model effects and non-Gaussian data. Unlike traditional linear model textbooks that focus on normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed and effects may be fixed or random.
Features:
* Provides a true introduction to linear modeling that assumes data need not be normally distributed and assumes random model effects to be the rule not an advanced exception;
* Emphasizes the connection between study design and all aspects of the model;
* Includes a chapter on GLMM-based power and sample size assessment—a critical tool for cost-effective design of research studies;
* Gives in-depth treatments of issues unique to generalized and mixed linear modeling, including conditional versus marginal modeling, broad versus narrow inference space, and data versus model-scale inference and reporting.
With numerous examples using SAS® PROC GLIMMIX, this book is ideal for graduate students in statistics, statistics professionals seeking to update their knowledge, and researchers new to the generalized linear model thought process. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling.