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Generalized linear mixed models : modern concepts, methods and applications

Author: Walter W Stroup
Publisher: Boca Raton : CRC Press, Taylor & Francis Group, [2013]
Series: Texts in statistical science.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"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  Read more...
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Details

Document Type: Book
All Authors / Contributors: Walter W Stroup
ISBN: 9781439815120 1439815127
OCLC Number: 742512037
Notes: "A Chapman & Hall book."
Description: xxv, 529 pages : illustrations ; 27 cm.
Contents: Note continued: 10.5. Summary --
Exercises --
11. Counts --
11.1. Introduction --
11.1.1. Count Data and the Poisson Distribution --
11.1.2. Example Comparing Pre-GLM ANOVA-Based Analysis to Poisson GLM --
11.2. Overdispersion in Count Data --
11.2.1. Overdispersion Defined --
11.2.2. Detecting Overdispersion --
11.2.3. Strategies --
11.2.3.1. Scale Parameter --
11.2.3.2. "What Would Fisher Do?" Revisited --
11.2.3.3. Alternative Distributions --
11.3. More on Alternative Distributions --
11.3.1. Negative Binomial --
11.3.2. Generalized Poisson --
11.4. Conditional and Marginal --
11.5. Too Many Zeroes --
11.5.1. Formal Description of Zero-Inflated and Hurdle Models --
11.5.2. GLMM for Poisson and Negative Binomial Zero-Inflated and Hurdle Models --
11.6. Summary --
Exercises --
12. Time-to-Event Data --
12.1. Introduction: Probability Concepts for Time-to-Event Data --
12.2. Gamma GLMMs --
12.2.1. Hierarchical (Split-Plot) Gamma GLMM --
12.2.1.1. What Happens If We Fit This Model Using a Gaussian LMM? --
12.2.1.2. Gamma Generalized Linear Model --
12.2.2. Response Surface for Time-to-Event: An Example Using the Box-Behnken Design --
12.2.2.1. Gaussian LMM --
12.2.2.2. Gamma GLMM --
12.3. GLMMs and Survival Analysis --
12.3.1. Basic Concepts and Terminology --
12.3.2. Exponential Survival GLMM for Uncensored Data --
12.3.3. Exponential Survival GLMM for Censored Data --
12.4. Summary --
13. Multinomial Data --
13.1. Overview --
13.2. Multinomial Data with Ordered Categories --
13.3. Nominal Categories: Generalized Logit Models --
13.4. Model Comparison --
13.5. Summary --
Exercises --
14. Correlated Errors, Part I: Repeated Measures --
14.1. Overview --
14.1.1. What Are Repeated Measures/Longitudinal Data --
14.1.2. Pre-GLMM Methods --
14.2. Gaussian Data: Correlation and Covariance Models for LMMs --
14.3. Covariance Model Selection --
14.3.1. Why Does It Matter? --
14.3.2. Covariance Model Selection Methods --
14.4. Non-Gaussian Case --
14.4.1. GEE-Type Models --
14.4.2. GLMMs --
14.5. Issues for Non-Gaussian Repeated Measures --
14.5.1. How Do Correlated Errors Arise? Deciding What We Are Modeling --
14.5.2. Covariance Model Selection and Non-Gaussian Repeated Measures --
14.5.3. Inference Space, Standard Errors, and Test Statistics --
14.6. Summary --
Exercises --
15. Correlated Errors, Part II: Spatial Variability --
15.1. Overview --
15.1.1. Types of Spatial Variability --
15.1.2. Pre-GLMM Methods --
15.1.2.1. Nearest-Neighbor Adjustment --
15.1.2.2. Blocking --
15.2. Gaussian Case with Covariance Model --
15.2.1. Covariance Model Selection --
15.2.2. Impact of Spatial Variability on Inference --
15.3. Spatial Covariance Modeling by Smoothing Spline --
15.4. Non-Gaussian Case --
15.4.1. Randomized Complete Block Model --
15.4.2. Incomplete Block Model --
15.4.3. GLIMMIX Statements --
15.4.3.1. RCB --
15.4.3.2. Lattice Incomplete Blocks --
15.4.4. GEE-Type "R-Side" Spatial Correlation Model --
15.4.5. "G-Side" Spatial Correlation Model --
15.4.5.1. G-Side Spatial Radial Smoothing Model --
15.4.5.2. Relevant Output --
15.5. Summary --
Exercise --
16. Power, Sample Size, and Planning --
16.1. Basics of GLMM-Based Power and Precision Analysis --
16.1.1. Essential GLMM Theory for Power and Precision Analysis --
16.1.2. Using SAS PROC GLIMMIX to Implement a Power Analysis --
16.2. Gaussian Example --
16.3. Power for Binomial GLMMs --
16.4. GLMM-Based Power Analysis for Count Data --
16.5. Power and Planning for Repeated Measures --
16.5.1. Straightforward Cases: Gaussian and One-Parameter Exponential Family --
16.5.2. On the Frontier: The Two-Parameter Exponential Family --
16.6. Summary --
Exercises --
Appendices: Essential Matrix Operations and Results --
Appendix A Matrix Operations --
Appendix B Distribution Theory for Matrices.
Series Title: Texts in statistical science.
Responsibility: Walter W. Stroup.

Abstract:

"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"--
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"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 Read more...

 
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