LEADER 05448nam 22006494a 450 001 9910830133703321 005 20230828213320.0 010 $a1-280-44838-5 010 $a9786610448388 010 $a0-470-00967-5 010 $a0-470-00966-7 035 $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(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$b[electronic resource] $e[mixed-effects modeling approaches] /$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 $a0-471-48350-8 320 $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 $a519.5/4 676 $a519.54 700 $aWu$b Hulin$01653704 701 $aZhang$b Jin-Ting$f1964-$01689680 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830133703321 996 $aNonparametric regression methods for longitudinal data analysis$94064913 997 $aUNINA