LEADER 01274nas 2200397- 450 001 9910894592903321 005 20190804010728.5 011 $a2084-7173 035 $a(DE-599)ZDB2863296-5 035 $a(OCoLC)885883498 035 $a(CKB)3460000000121475 035 $a(CONSER)--2016207115 035 $a(EXLCZ)993460000000121475 100 $a20140709a20149999 s-- - 101 0 $aeng 135 $aur|nu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMethods in next generation sequencing 210 1$aWarsaw, Poland :$cDE GRUYTER OPEN 300 $aRefereed/Peer-reviewed 300 $a"Emerging Science." 606 $aNucleotide sequence$vPeriodicals 606 $aSequence alignment (Bioinformatics)$vPeriodicals 606 $aNucleotide sequence$2fast$3(OCoLC)fst01041106 606 $aSequence alignment (Bioinformatics)$2fast$3(OCoLC)fst01747147 608 $aPeriodicals.$2fast 615 0$aNucleotide sequence 615 0$aSequence alignment (Bioinformatics) 615 7$aNucleotide sequence. 615 7$aSequence alignment (Bioinformatics) 906 $aJOURNAL 912 $a9910894592903321 996 $aMethods in next generation sequencing$94268603 997 $aUNINA LEADER 05540nam 22006974a 450 001 9911019449603321 005 20200520144314.0 010 $a9786610448388 010 $a9781280448386 010 $a1280448385 010 $a9780470009673 010 $a0470009675 010 $a9780470009666 010 $a0470009667 035 $a(CKB)1000000000355635 035 $a(EBL)257544 035 $a(OCoLC)475973641 035 $a(SSID)ssj0000212012 035 $a(PQKBManifestationID)11174860 035 $a(PQKBTitleCode)TC0000212012 035 $a(PQKBWorkID)10136427 035 $a(PQKB)11634071 035 $a(MiAaPQ)EBC257544 035 $a(Perlego)2765093 035 $a(EXLCZ)991000000000355635 100 $a20051202d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNonparametric regression methods for longitudinal data analysis $e[mixed-effects modeling approaches] /$fHulin Wu, Jin-Ting Zhang 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (401 p.) 225 1 $aWiley series in probability and statistics 300 $aSubtitle from cover. 311 08$a9780471483502 311 08$a0471483508 320 $aIncludes bibliographical references (p. 347-361) and index. 327 $aNonparametric Regression Methods for Longitudinal Data Analysis; Preface; Contents; Acronyms; 1 Introduction; 1.1 Motivating Longitudinal Data Examples; 1.1.1 Progesterone Data; 1.1.2 ACTG 388 Data; 1.1.3 MACS Data; 1.2 Mixed-Effects Modeling: from Parametric to Nonparametric; 1.2.1 Parametric Mixed-Effects Models; 1.2.2 Nonparametric Regression and Smoothing; 1.2.3 Nonparametric Mixed-Effects Models; 1.3 Scope of the Book; 1.3.1 Building Blocks of the NPME Models; 1.3.2 Fundamental Development of the NPME Models; 1.3.3 Further Extensions of the NPME Models 327 $a1.4 Implementation of Methodologies1.5 Options for Reading This Book; 1.6 Bibliographical Notes; 2 Parametric Mixed-Effects Models; 2.1 Introduction; 2.2 Linear Mixed-Effects Model; 2.2.1 Model Specification; 2.2.2 Estimation of Fixed and Random-Effects; 2.2.3 Bayesian Interpretation; 2.2.4 Estimation of Variance Components; 2.2.5 The EM-Algorithms; 2.3 Nonlinear Mixed-Effects Model; 2.3.1 Model Specification; 2.3.2 Two-Stage Method; 2.3.3 First-Order Linearization Method; 2.3.4 Conditional First-Order Linearization Method; 2.4 Generalized Mixed-Effects Model 327 $a2.4.1 Generalized Linear Mixed-Effects Model2.4.2 Examples of GLME Model; 2.4.3 Generalized Nonlinear Mixed-Effects Model; 2.5 Summary and Bibliographical Notes; 2.6 Appendix: Proofs; 3 Nonparametric Regression Smoothers; 3.1 Introduction; 3.2 Local Polynomial Kernel Smoother; 3.2.1 General Degree LPK Smoother; 3.2.2 Local Constant and Linear Smoothers; 3.2.3 Kernel Function; 3.2.4 Bandwidth Selection; 3.2.5 An Illustrative Example; 3.3 Regression Splines; 3.3.1 Truncated Power Basis; 3.3.2 Regression Spline Smoother; 3.3.3 Selection of Number and Location of Knots 327 $a3.3.4 General Basis-Based Smoother3.4 Smoothing Splines; 3.4.1 Cubic Smoothing Splines; 3.4.2 General Degree Smoothing Splines; 3.4.3 Connection between a Smoothing Spline and a LME Model; 3.4.4 Connection between a Smoothing Spline and a State-Space Model; 3.4.5 Choice of Smoothing Parameters; 3.5 Penalized Splines; 3.5.1 Penalized Spline Smoother; 3.5.2 Connection between a Penalized Spline and a LME Model; 3.5.3 Choice of the Knots and Smoothing Parameter Selection; 3.5.4 Extension; 3.6 Linear Smoother; 3.7 Methods for Smoothing Parameter Selection; 3.7.1 Goodness of Fit 327 $a3.7.2 Model Complexity3.7.3 Cross-Validation; 3.7.4 Generalized Cross-Validation; 3.7.5 Generalized Maximum Likelihood; 3.7.6 Akaike Information Criterion; 3.7.7 Bayesian Information Criterion; 3.8 Summary and Bibliographical Notes; 4 Local Polynomial Methods; 4.1 Introduction; 4.2 Nonparametric Population Mean Model; 4.2.1 Naive Local Polynomial Kernel Method; 4.2.2 Local Polynomial Kernel GEE Method; 4.2.3 Fan-Zhang 's Two-step Method; 4.3 Nonparametric Mixed-Effects Model; 4.4 Local Polynomial Mixed-Effects Modeling; 4.4.1 Local Polynomial Approximation; 4.4.2 Local Likelihood Approach 327 $a4.4.3 Local Marginal Likelihood Estimation 330 $aIncorporates mixed-effects modeling techniques for more powerful and efficient methodsThis book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented.With its logical structure and organization, beginning with basic principles, the text develops t 410 0$aWiley series in probability and statistics. 606 $aNonparametric statistics 606 $aLongitudinal method$xMathematical models 615 0$aNonparametric statistics. 615 0$aLongitudinal method$xMathematical models. 676 $a519.5/4 700 $aWu$b Hulin$01840138 701 $aZhang$b Jin-Ting$f1964-$01840139 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019449603321 996 $aNonparametric regression methods for longitudinal data analysis$94419642 997 $aUNINA