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 LinearRead 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

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zlib/Science (General)/Stroup, Walter W/Generalized linear mixed models: modern concepts, methods and applications_6029873.pdf

Walter W. Stroup

CRC Press, Taylor & Francis Group

CRC Press LLC

Texts in statistical science, First edition, Boca Raton, FL, 2012

Texts in statistical science, Boca Raton, 2013

United States, United States of America

CRC Press LLC, Boca Raton, 2013

1, 2016

kolxo3 -- 93

lg2804332

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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 Scale vs. Model ScaleIssue II: Inference SpaceIssue III: Conditional and Marginal ModelsSummaryPART II Estimation and Inference EssentialsEstimationIntroductionEssential BackgroundFixed Effects OnlyGaussian Mixed ModelsGeneralized Linear Mixed ModelsSummaryInference, Part I: Model EffectsIntroductionEssential BackgroundApproaches to TestingInference Using Model-Based StatisticsInference Using Empirical Standard ErrorSummary of Main Ideas and General Guidelines for ImplementationInference, Part II: Covariance ComponentsIntroductionFormal Testing of Covariance ComponentsFit Statistics to Compare Covariance ModelsInterval EstimationSummaryPART III Working with GLMMsTreatment and Explanatory Variable StructureTypes of Treatment StructuresTypes of Estimable FunctionsMultiple Factor Models: OverviewMultifactor Models with All Factors QualitativeMultifactor: Some Factors Qualitative, Some Factors QuantitativeMultifactor: All Factors QuantitativeSummaryMultilevel ModelsTypes of Design Structure: Single- and Multilevel Models DefinedTypes of Multilevel Models and How They AriseRole of Blocking in Multilevel ModelsWorking with Multilevel DesignsMarginal and Conditional Multilevel ModelsSummaryBest Linear Unbiased PredictionReview of Estimable and Predictable

"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"-- Provided by publisher

Front Cover......Page 1
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications......Page 6
Copyright......Page 7
Table of Contents......Page 8
Preface......Page 16
Acknowledgments......Page 26
Part I: The Big Picture......Page 28
1. Modeling Basics......Page 30
2. Design Matters......Page 52
3. Setting the Stage......Page 92
Part II: Estimation and Inference Essentials......Page 146
4. Estimation......Page 148
5. Inference, Part I: Model Effects......Page 176
6. Inference, Part II: Covariance Components......Page 206
Part III: Working with GLMMs......Page 228
7. Treatment and Explanatory Variable Structure......Page 230
8. Multilevel Models......Page 266
9. Best Linear Unbiased Prediction......Page 298
10. Rates and Proportions......Page 326
11. Counts......Page 362
12. Time-to-Event Data......Page 402
13. Multinomial Data......Page 424
14. Correlated Errors, Part I: Repeated Measures......Page 440
15. Correlated Errors, Part II: Spatial Variability......Page 470
16. Power, Sample Size, and Planning......Page 494
Appendices: Essential Matrix Operations and Results......Page 526
Appendix A: Matrix Operations......Page 528
Appendix B: Distribution Theory for Matrices......Page 536
References......Page 540
Back Cover......Page 547

With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model 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.

2020-10-11