Generalized Linear Mixed Models: Modern Concepts, Methods and Applications

The second edition of "Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications" provides an updated introduction to linear modeling using the generalized linear mixed model (GLMM). It covers all members of the GLMM family, including classical and advanced models, and incorporates lessons learned from experience and research. The book discusses the difference between marginal and conditional models, inference space, and when each type of model is appropriate. It also provides a brief introduction to Bayesian methods for GLMMs. The author, Walt Stroup, is an Emeritus Professor of Statistics, and the co-author, Marina Ptukhina, is an Associate Professor of Statistics at Whitman College.

Format: Hardback
Length: 648 pages
Publication date: 21 May 2024
Publisher: Taylor & Francis Inc

Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) serves as an updated introduction to linear modeling, utilizing the generalized linear mixed model (GLMM) as the overarching conceptual framework. This book is particularly valuable for students new to statistical modeling, as it provides a comprehensive overview of linear modeling, its relationship with statistical design and mathematical statistics, and its broader applications. For readers with experience in statistical practice but new to GLMMs, the book offers a thorough introduction to GLMM methodology and its underlying theory.

Unlike textbooks that specialize in classical linear models, generalized linear models, or mixed models, this book encompasses all of these as members of the unified GLMM family of linear models. In addition to essential theory and methodology, the book features a rich collection of examples using SAS® software to illustrate GLMM practice. This second edition has been revised to reflect lessons learned and experiences gained regarding best practices and modeling choices encountered by GLMM practitioners. Notably, two new chapters have been added to focus specifically on Bayesian methods for GLMMs.

Key Features:

Comprehensive Coverage: This book covers all members of the GLMM family, including classical and advanced models. It provides a comprehensive perspective on statistical modeling, enabling students to understand the broader range of models available.

Up-to-Date Examples: Incorporates lessons learned from experience and ongoing research to provide up-to-date examples of best practices. The book includes detailed illustrations of how to translate study design into appropriate models and showcases the application of GLMM methods to improve planning and design.

Statistical Design and Modeling Connections: Discusses the relationship between statistical design and modeling, providing guidelines for translating study design into appropriate models. It also offers in-depth illustrations of how to implement these guidelines, leveraging the power of GLMM methods to enhance research outcomes.

Marginal and Conditional Modeling: Explores the distinction between marginal and conditional modeling, highlighting their respective advantages and applications. The book provides insights into when each modeling approach is most suitable and demonstrates how GLMMs can be used to incorporate both perspectives into a comprehensive analysis.

By presenting a comprehensive and up-to-date introduction to GLMMs, Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) serves as a valuable resource for students, researchers, and practitioners in the fields of statistics, psychology, and other applied disciplines. Its comprehensive coverage, practical examples, and connections between statistical design and modeling make it an essential tool for anyone seeking to enhance their understanding and application of linear modeling techniques.

Weight: 1358g
Dimension: 260 x 184 x 42 (mm)
ISBN-13: 9781498755566
Edition number: 2 ed