Generalized Linear Mixed Models : Modern Concepts, Methods and Applications

✍ Scribed by Stroup, Walter W

• Publisher: CRC Press
• Year: 2012
• Tongue: English
• Leaves: 547
• Series: Chapman & Hall/CRC Texts in Statistical Science
• Category: Library

✦ Synopsis

PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data. Read more...

Abstract: PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data

✦ Table of Contents

Content: Front Cover
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications
Copyright
Table of Contents
Preface
Acknowledgments
Part I: The Big Picture
1. Modeling Basics
2. Design Matters
3. Setting the Stage
Part II: Estimation and Inference Essentials
4. Estimation
5. Inference, Part I: Model Effects
6. Inference, Part II: Covariance Components
Part III: Working with GLMMs
7. Treatment and Explanatory Variable Structure
8. Multilevel Models
9. Best Linear Unbiased Prediction
10. Rates and Proportions
11. Counts
12. Time-to-Event Data
13. Multinomial Data 14. Correlated Errors, Part I: Repeated Measures15. Correlated Errors, Part II: Spatial Variability
16. Power, Sample Size, and Planning
Appendices: Essential Matrix Operations and Results
Appendix A: Matrix Operations
Appendix B: Distribution Theory for Matrices
References
Back Cover

✦ Subjects

Математика;Теория вероятностей и математическая статистика;Математическая статистика;