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200 10$aGeneralized linear mixed models $emodern concepts, methods and applications /$fby Walter W. Stroup
205   $aFirst edition.
210  1$aBoca Raton, FL :$cCRC Press, an imprint of Taylor and Francis,$d2012.
215   $a1 online resource (547 p.)
225 1 $aChapman & Hall/CRC Texts in Statistical Science
225 0 $aA Chapman & Hall Book
300   $aDescription based upon print version of record.
311   $a1-4398-1512-7 
320   $aIncludes bibliographical references.
327   $aFront 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
327   $a14. 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
330 3 $aGeneralized 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.
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