Generalized Linear Mixed Models: Modern Concepts, Methods and Applications / Edition 2

by Walter W. Stroup, Marina Ptukhina, Julie Garai

Overview

Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture – linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory.

Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS® software to illustrate GLMM practice. This second edition is updated to reflect lessons learned and experience gained regarding best practices and modeling choices faced by GLMM practitioners. New to this edition are two chapters focusing on Bayesian methods for GLMMs.

Key Features:

• Most statistical modeling books cover classical linear models or advanced generalized and mixed models; this book covers all members of the GLMM family – classical and advanced models
• Incorporates lessons learned from experience and on-going research to provide up-to-date examples of best practices
• Illustrates connections between statistical design and modeling: guidelines for translating study design into appropriate model and in-depth illustrations of how to implement these guidelines; use of GLMM methods to improve planning and design
• Discusses the difference between marginal and conditional models, differences in the inference space they are intended to address and when each type of model is appropriate
• In addition to likelihood-based frequentist estimation and inference, provides a brief introduction to Bayesian methods for GLMMs

Product Details

ISBN-13:	9781498755566
Publisher:	Taylor & Francis
Publication date:	05/21/2024
Series:	Chapman & Hall/CRC Texts in Statistical Science
Edition description:	2nd ed.
Pages:	668
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions.

Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute."

Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor’s degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children and playing the trombone.

Table of Contents

Preface to the Second Edition

Part 1: Essential Background

1. Modeling Basics

2. Design Matters

3. Setting the Stage

Part 2: Estimation and Inference Theory

4. Pre-GLMM Estimation and Inference Basics

5. GLMM Estimation

6. Inference, Part I

7. Inference, Part II

Part 3: Applications

8. Treatment and Explanatory Variable Structure

9. Multi-Level Models

10. Best Linear Unbiased Prediction

11. Counts

12. Rates and Proportions

13. Zero-inflated and Hurdle Models

14. Multinomial Data

15. Time-to-Event Data

16. Smoothing Splines and Additive Models

17. Correlated Errors, part 1: Repeated Measures

18. Correlated Errors, part 2: Spatial Variability

19. Bayesian Implementation of GLMM

20. Four Bayesian GLMM Examples

21. Precision, Power, Sample Size and Planning

Editorial Reviews

"The book focuses on data-driven modeling and design processes, and it provides a context for extending traditional linear model thinking to generalised linear mixed modeling. This is a very sound text which teachers of any course on GLMMs should consider adopting."
—Erkki P. Liski, International Statistical Review (2013), 81

"Walter Stroup is a leading authority on GLMMs for applied statisticians, especially as implemented in the SAS programming environment. He offers a thorough, engaging, and opinionated treatment of the subject … I found the ‘fully general’ GLMM approach to modeling and design issues (Chapters 1 and 2) to be quite illuminating. … it is best to use this text in conjunction with SAS. Prospective readers without current access to SAS will be pleased to know that a reasonable level of access to SAS is now available at no cost to students and teachers on the web … If the reader prefers to work with GLMMs in the free, powerful, and state-of-the-art R environment, then he/she should supplement this text with some others that are built around R. I myself had good luck using Stroup’s text along with Julian Faraway’s two books Linear Models with R and Expanding the Linear Model with R, both published by CRC Press."
—Homer White, MAA Reviews, June 2013

"… for SAS users concerned with the analysis of trials, it is a very good resource. There are excellent discussions on many important concepts such as likelihood ratio testing and model selection criteria. PROC GLIMMIX is a powerful procedure implementing the rich family of GLMMs, and this book gives coverage to a wide variety of models with ample software illustration."
—Gillian Z. Heller, Australian & New Zealand Journal of Statistics, 2013

From the Publisher