LEADER 05357nam 2200721 a 450 001 9910141259703321 005 20230801223456.0 010 $a1-118-35887-2 010 $a1-280-87995-5 010 $a9786613721266 010 $a1-118-35880-5 010 $a1-118-35888-0 010 $a1-118-35943-7 035 $a(CKB)2670000000208518 035 $a(EBL)954614 035 $a(OCoLC)798536286 035 $a(SSID)ssj0000676769 035 $a(PQKBManifestationID)11474980 035 $a(PQKBTitleCode)TC0000676769 035 $a(PQKBWorkID)10683649 035 $a(PQKB)10317098 035 $a(MiAaPQ)EBC954614 035 $a(DLC) 2012018371 035 $a(Au-PeEL)EBL954614 035 $a(CaPaEBR)ebr10579514 035 $a(CaONFJC)MIL372126 035 $a(EXLCZ)992670000000208518 100 $a20120503d2012 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBasic and advanced Bayesian structural equation modeling$b[electronic resource] $ewith applications in the medical and behavioral sciences /$fSik-Yum Lee and Xin-Yuan Song 210 $aHoboken $cWiley$d2012 215 $a1 online resource (397 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-66952-7 320 $aIncludes bibliographical references and index. 327 $aBasic and Advanced Bayesian Structural Equation Modeling; Contents; About the authors; Preface; 1 Introduction; 1.1 Observed and latent variables; 1.2 Structural equation model; 1.3 Objectives of the book; 1.4 The Bayesian approach; 1.5 Real data sets and notation; Appendix 1.1: Information on real data sets; References; 2 Basic concepts and applications of structural equation models; 2.1 Introduction; 2.2 Linear SEMs; 2.2.1 Measurement equation; 2.2.2 Structural equation and one extension; 2.2.3 Assumptions of linear SEMs; 2.2.4 Model identification; 2.2.5 Path diagram 327 $a2.3 SEMs with fixed covariates 2.3.1 The model; 2.3.2 An artificial example; 2.4 Nonlinear SEMs; 2.4.1 Basic nonlinear SEMs; 2.4.2 Nonlinear SEMs with fixed covariates; 2.4.3 Remarks; 2.5 Discussion and conclusions; References; 3 Bayesian methods for estimating structural equation models; 3.1 Introduction; 3.2 Basic concepts of the Bayesian estimation and prior distributions; 3.2.1 Prior distributions; 3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs; 3.3 Posterior analysis using Markov chain Monte Carlo methods; 3.4 Application of Markov chain Monte Carlo methods 327 $a3.5 Bayesian estimation via WinBUGS Appendix 3.1: The gamma, inverted gamma, Wishart, and inverted Wishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [?|Y,?] and [?|Y,?]; Appendix 3.4: Conditional distributions [?|Y,?] and [?|Y,?] in nonlinear SEMs with covariates; Appendix 3.5: WinBUGS code; Appendix 3.6: R2WinBUGS code; References; 4 Bayesian model comparison and model checking; 4.1 Introduction; 4.2 Bayes factor; 4.2.1 Path sampling; 4.2.2 A simulation study; 4.3 Other model comparison statistics 327 $a4.3.1 Bayesian information criterion and Akaike information criterion 4.3.2 Deviance information criterion; 4.3.3 L?-measure; 4.4 Illustration; 4.5 Goodness of fit and model checking methods; 4.5.1 Posterior predictive p-value; 4.5.2 Residual analysis; Appendix 4.1: WinBUGS code; Appendix 4.2: R code in Bayes factor example; Appendix 4.3: Posterior predictive p-value for model assessment; References; 5 Practical structural equation models; 5.1 Introduction; 5.2 SEMs with continuous and ordered categorical variables; 5.2.1 Introduction; 5.2.2 The basic model; 5.2.3 Bayesian analysis 327 $a5.2.4 Application: Bayesian analysis of quality of life data 5.2.5 SEMs with dichotomous variables; 5.3 SEMs with variables from exponential family distributions; 5.3.1 Introduction; 5.3.2 The SEM framework with exponential family distributions; 5.3.3 Bayesian inference; 5.3.4 Simulation study; 5.4 SEMs with missing data; 5.4.1 Introduction; 5.4.2 SEMs with missing data that are MAR; 5.4.3 An illustrative example; 5.4.4 Nonlinear SEMs with nonignorable missing data; 5.4.5 An illustrative real example 327 $aAppendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables 330 $a"This book introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject's recent advances"--$cProvided by publisher. 410 0$aWiley series in probability and statistics. 606 $aStructural equation modeling 606 $aBayesian statistical decision theory 615 0$aStructural equation modeling. 615 0$aBayesian statistical decision theory. 676 $a519.5/3 686 $aMAT029000$2bisacsh 700 $aLee$b Sik-Yum$0308566 701 $aSong$b Xin-Yuan$0891931 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141259703321 996 $aBasic and advanced Bayesian structural equation modeling$91992023 997 $aUNINA