LEADER 05329nam 2200649Ia 450 001 9910144743403321 005 20170815114221.0 010 $a1-281-20376-9 010 $a9786611203764 010 $a0-470-18646-1 010 $a0-470-18645-3 035 $a(CKB)1000000000377294 035 $a(EBL)331560 035 $a(OCoLC)212406944 035 $a(SSID)ssj0000204216 035 $a(PQKBManifestationID)11221191 035 $a(PQKBTitleCode)TC0000204216 035 $a(PQKBWorkID)10188163 035 $a(PQKB)10955612 035 $a(MiAaPQ)EBC331560 035 $a(EXLCZ)991000000000377294 100 $a20070602d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModern applied U-statistics$b[electronic resource] /$fJeanne Kowalski; Xin M. Tu 210 $aHoboken, NJ $cWiley Pub.$d2008 215 $a1 online resource (402 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-68227-6 320 $aIncludes bibliographical references and index. 327 $aModern Applied U-Statistics; Contents; Preface; 1 Preliminaries; 1.1 Introduction; 1.1.1 The Linear Regression Model; 1.1.2 The Product-Moment Correlation; 1.1.3 The Rank-Based Mann-Whitney-Wilcoxon Test; 1.2 Measurability and Measure Space; 1.2.1 Measurable Space; 1.2.2 Measure Space; 1.3 Measurable Function and Integration; 1.3.1 Measurable Functions; 1.3.2 Convergence of Sequence of Measurable Functions; 1.3.3 Integration of Measurable Functions; 1.3.4 Integration of Sequences of Measurable Functions; 1.4 Probability Space and Random Variables; 1.4.1 Probability Space 327 $a1.4.2 Random Variables1.4.3 Random Vectors; 1.5 Distribution Function and Expectation; 1.5.1 Distribution Function; 1.5.2 Joint Distribution of Random Vectors; 1.5.3 Expectation; 1.5.4 Conditional Expectation; 1.6 Convergence of Random Variables and Vectors; 1.6.1 Modes of Convergence; 1.6.2 Convergence of Sequence of I.I.D. Random Variables; 1.6.3 Rate of Convergence of Random Sequence; 1.6.4 Stochastic op (.) and Op (.); 1.7 Convergence of Functions of Random Vectors; 1.7.1 Convergence of Functions of Random Variables; 1.7.2 Convergence of Functions of Random Vectors; 1.8 Exercises 327 $a2 Models for Cross-Sectional Data2.1 Parametric Regression Models; 2.1.1 Linear Regression Model; 2.1.2 Inference for Linear Models; 2.1.3 General Linear Hypothesis; 2.1.4 Generalized Linear Models; 2.1.5 Inference for Generalized Linear Models; 2.2 Distribution-Free (Semiparametric) Models; 2.2.1 Distribution-Free Generalized Linear Models; 2.2.2 Inference for Generalized Linear Models; 2.3 Exercises; 3 Univariate U-Statistics; 3.1 U-Statistics and Associated Models; 3.1.1 One Sample U-Statistics; 3.1.2 Two-Sample and General K Sample U-Statistics 327 $a3.1.3 Representation of U-Statistic by Order Statistic3.1.4 Martingale Structure of U-Statistic; 3.2 Inference for U-Statistics; 3.2.1 Projection of U-statistic; 3.2.2 Asymptotic Distribution of One-Group U-Statistic; 3.2.3 Asymptotic Distribution of K-Group U-Statistic; 3.3 Exercises; 4 Models for Clustered Data; 4.1 Longitudinal versus Cross-Sectional Designs; 4.2 Parametric Models; 4.2.1 Multivariate Normal Distribution Based Models; 4.2.2 Linear Mixed-Effects Model; 4.2.3 Generalized Linear Mixed-Effects Models; 4.2.4 Maximum Likelihood Inference; 4.3 Distribution-Free Models 327 $a4.3.1 Distribution-Free Models for Longitudinal Data4.3.2 Inference for Distribution-Free Models; 4.4 Missing Data; 4.4.1 Inference for Parametric Models; 4.4.2 Inference for Distribution-Free Models; 4.5 GEE II for Modeling Mean and Variance; 4.6 Structural Equations Models; 4.6.1 Path Diagrams and Models; 4.6.2 Maximum Likelihood Inference; 4.6.3 GEE-Based Inference; 4.7 Exercises; 5 Multivariate U-Statistics; 5.1 Models for Cross-Sectional Study Designs; 5.1.1 One Sample Multivariate U-Statistics; 5.1.2 General K Sample Multivariate U-Statistics; 5.2 Models for Longitudinal Study Designs 327 $a5.2.1 Inference in the Absence of Missing Data 330 $aA timely and applied approach to the newly discovered methods and applications of U-statisticsBuilt on years of collaborative research and academic experience, Modern Applied U-Statistics successfully presents a thorough introduction to the theory of U-statistics using in-depth examples and applications that address contemporary areas of study including biomedical and psychosocial research. Utilizing a ""learn by example"" approach, this book provides an accessible, yet in-depth, treatment of U-statistics, as well as addresses key concepts in asymptotic theory by integrating translatio 410 0$aWiley series in probability and statistics. 606 $aU-statistics 606 $aMathematical statistics 608 $aElectronic books. 615 0$aU-statistics. 615 0$aMathematical statistics. 676 $a519.52 700 $aKowalski$b Jeanne$0928919 701 $aTu$b Xin M$0520698 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144743403321 996 $aModern applied U-statistics$92087728 997 $aUNINA