05357nam 2200721 a 450 991014125970332120230801223456.01-118-35887-21-280-87995-597866137212661-118-35880-51-118-35888-01-118-35943-7(CKB)2670000000208518(EBL)954614(OCoLC)798536286(SSID)ssj0000676769(PQKBManifestationID)11474980(PQKBTitleCode)TC0000676769(PQKBWorkID)10683649(PQKB)10317098(MiAaPQ)EBC954614(DLC) 2012018371(Au-PeEL)EBL954614(CaPaEBR)ebr10579514(CaONFJC)MIL372126(EXLCZ)99267000000020851820120503d2012 uy 0engurcn|||||||||txtccrBasic and advanced Bayesian structural equation modeling[electronic resource] with applications in the medical and behavioral sciences /Sik-Yum Lee and Xin-Yuan SongHoboken Wiley20121 online resource (397 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-470-66952-7 Includes bibliographical references and index.Basic 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 diagram2.3 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 methods3.5 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 [Ω|Y,θ] and [θ|Y,Ω]; Appendix 3.4: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω] 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 statistics4.3.1 Bayesian information criterion and Akaike information criterion 4.3.2 Deviance information criterion; 4.3.3 Lν-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 analysis5.2.4 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 exampleAppendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables"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"--Provided by publisher.Wiley series in probability and statistics.Structural equation modelingBayesian statistical decision theoryStructural equation modeling.Bayesian statistical decision theory.519.5/3MAT029000bisacshLee Sik-Yum308566Song Xin-Yuan891931MiAaPQMiAaPQMiAaPQBOOK9910141259703321Basic and advanced Bayesian structural equation modeling1992023UNINA