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010   $a9781071612828
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024 7 $a10.1007/978-1-0716-1282-8
035   $a(CKB)4100000011807207
035   $a(MiAaPQ)EBC6524983
035   $a(Au-PeEL)EBL6524983
035   $a(OCoLC)1243554583
035   $a(PPN)254719716
035   $a(DE-He213)978-1-0716-1282-8
035   $a(EXLCZ)994100000011807207
100   $a20210322d2021      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aLinear and Generalized Linear Mixed Models and Their Applications /$fby Jiming Jiang, Thuan Nguyen
205   $a2nd ed. 2021.
210  1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2021.
215   $a1 online resource (352 pages) $cillustrations
225 1 $aSpringer Series in Statistics,$x2197-568X
311 08$a9781071612811 
311 08$a1071612816 
320   $aIncludes bibliographical references and index.
327   $a1. Linear Mixed Models: Part I -- 2. Linear Mixed Models: Part II -- 3. Generalized Linear Mixed Models: Part I -- 4. Generalized Linear Mixed Models: Part II.
330   $aNow in its second edition, this book covers two major classes of mixed effects models?linear mixed models and generalized linear mixed models?and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics. This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduatecourses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra.
410  0$aSpringer Series in Statistics,$x2197-568X
606   $aBiometry
606   $aProbabilities
606   $aStatistics
606   $aPublic health
606   $aNumerical analysis
606   $aPopulation genetics
606   $aBiostatistics
606   $aProbability Theory
606   $aStatistical Theory and Methods
606   $aPublic Health
606   $aNumerical Analysis
606   $aPopulation Genetics
615  0$aBiometry.
615  0$aProbabilities.
615  0$aStatistics.
615  0$aPublic health.
615  0$aNumerical analysis.
615  0$aPopulation genetics.
615 14$aBiostatistics.
615 24$aProbability Theory.
615 24$aStatistical Theory and Methods.
615 24$aPublic Health.
615 24$aNumerical Analysis.
615 24$aPopulation Genetics.
676   $a519.5
700   $aJiang$b Jiming$0614598
702   $aNguyen$b Thuan
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910484963903321
996   $aLinear and generalized linear mixed models and their applications$91891947
997   $aUNINA