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Full Description
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.
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.
See Professor Stroup discuss the book.
Contents
PART I The Big Picture: Modeling Basics. Design Matters. Setting the Stage. PART II Estimation and Inference Essentials: Estimation. Inference, Part I: Model Effects. Inference, Part II: Covariance Components. PART III Working with GLMMs: Treatment and Explanatory Variable Structure. Multilevel Models. Best Linear Unbiased Prediction. Rates and Proportions. Counts. Time-to-Event Data. Multinomial Data. Correlated Errors, Part I: Repeated Measures. Correlated Errors, Part II: Spatial Variability. Power, Sample Size, and Planning. Appendices. References. Index.
