LEADER 03161oam 2200613I 450 001 9910797028903321 005 20230801234511.0 010 $a0-429-07529-4 010 $a1-4398-1513-5 024 7 $a10.1201/b13151 035 $a(CKB)3710000000391428 035 $a(EBL)1570060 035 $a(OCoLC)908078409 035 $a(SSID)ssj0001458817 035 $a(PQKBManifestationID)12555254 035 $a(PQKBTitleCode)TC0001458817 035 $a(PQKBWorkID)11456419 035 $a(PQKB)10866872 035 $a(MiAaPQ)EBC1570060 035 $a(Au-PeEL)EBL1570060 035 $a(CaPaEBR)ebr11167009 035 $a(OCoLC)935230613 035 $a(EXLCZ)993710000000391428 100 $a20180706d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$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 $aGeneralized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider. 410 0$aTexts in statistical science. 606 $aLinear models (Statistics) 615 0$aLinear models (Statistics) 676 $a519.5/36 686 $aMAT029000$2bisacsh 700 $aStroup$b Walter W$g(Walter Whitney),$0520691 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910797028903321 996 $aGeneralized linear mixed models$9834392 997 $aUNINA