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. It provides a comprehensive introduction to GLMM methodology.
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. It provides a comprehensive introduction to GLMM methodology.
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.
Inhaltsangabe
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
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
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