| Description |
1 online resource |
| Series |
Wiley Series in Probability and Statistics |
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Wiley series in probability and statistics.
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| Contents |
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 diagram |
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2.3 SEMs with fixed covariates2.3.1 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 |
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3.5 Bayesian estimation via WinBUGSAppendix 3.1: The gamma, inverted gamma, Wishart, and invertedWishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [Omega, gamma, theta] and [Omega, gamma, theta]; Appendix 3.4: Conditional distributions [Omega, gamma, theta] and [Omega, gamma, theta] 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 |
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5.2.4 Application: Bayesian analysis of quality of life data5.2.5 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 |
| Summary |
This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables.Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced |
| Notes |
Appendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables |
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Description based upon print version of record |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Electronic version is available via Wiley InterScience (Online service |
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Mode of access: World Wide Web |
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Description based on print version record and CIP data provided by publisher |
| Subject |
Structural equation modeling.
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|
Bayesian statistical decision theory.
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| Form |
Electronic book
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| Author |
Lee, Sik-Yum.
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Wiley InterScience (Online service)
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| LC no. |
2012018371 |
| ISBN |
1118358880 (electronic bk.) |
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1118359437 |
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9781118358887 (electronic bk.) |
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9781118359433 |
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