Lee Structural Equation Modeling

A Bayesian Approach
1. Auflage 2007
ISBN: 978-0-470-02424-9
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 458 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

***Winner of the 2008 Ziegel Prize for outstanding new book ofthe year*** Structural equation modeling (SEM) is a powerful multivariatemethod allowing the evaluation of a series of simultaneoushypotheses about the impacts of latent and manifest variables onother variables, taking measurement errors into account. As SEMshave grown in popularity in recent years, new models andstatistical methods have been developed for more accurate analysisof more complex data. A Bayesian approach to SEMs allows the use ofprior information resulting in improved parameter estimates, latentvariable estimates, and statistics for model comparison, as well asoffering more reliable results for smaller samples.
Structural Equation Modeling introduces the Bayesianapproach to SEMs, including the selection of prior distributionsand data augmentation, and offers an overview of thesubject's recent advances.
* Demonstrates how to utilize powerful statistical computingtools, including the Gibbs sampler, the Metropolis-Hastingalgorithm, bridge sampling and path sampling to obtain the Bayesianresults.
* Discusses the Bayes factor and Deviance Information Criterion(DIC) for model comparison.
* Includes coverage of complex models, including SEMs withordered categorical variables, and dichotomous variables, nonlinearSEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs withmissing data, SEMs with variables from an exponential family ofdistributions, and some of their combinations.
* Illustrates the methodology through simulation studies andexamples with real data from business management, education,psychology, public health and sociology.
* Demonstrates the application of the freely available softwareWinBUGS via a supplementary website featuring computer code anddata sets.
Structural Equation Modeling: A Bayesian Approach is amulti-disciplinary text ideal for researchers and students in manyareas, including: statistics, biostatistics, business, education,medicine, psychology, public health and social science.

Autoren/Hrsg.

Lee, Sik-Yum

Inhaltsverzeichnis

About the Author.
Preface.
Chapter 1. Introduction.
Chapter 2. Some Basic Structural Equation Models.
Chapter 3. Covariance Structure Analysis.
Chapter 4. Bayesian Estimation of Structural EquationModels.
Chapter 5. Model Comparison and Model Checking.
Chapter 6. Structural Equation Models with Continuous andOrdered Categorical Variables.
Chapter 7. Structural Equation Models with DichotomousVariables.
Chapter 8. Nonlinear Structural Equation Models.
Chapter 9. Two-level Nonlinear Structural Equation Models.
Chapter 10. Multisample Analysis of Structural EquationModels.
Chapter 11. Finite Mixtures in Structural Equation Models.
Chapter 12. Structural Equation Models with Missing Data.
Chapter 13. Structural Equation Models with Exponential Familyof Distributions.
Chapter 14. Conclusion.
Index.

Über Autor(innen)

Sik-Yum Lee is a professor of statistics at the Chinese University of Hong Kong. He earned his Ph.D. in biostatistics at the University of California, Los Angeles, USA. He received a distinguished service award from the International Chinese Statistical Association, is a former president of the Hong Kong Statistical Society, and is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association. He serves as Associate Editor for Psychometrika and Computational Statistics & Data Analysis, and as a member of the Editorial Board of British Journal of Mathematical and Statistical Psychology, Structural Equation Modeling, Handbook of Computing and Statistics with Applications and Chinese Journal of Medicine. his research interests are in structural equation models, latent variable models, Bayesian methods and statistical diagnostics. he is editor of Handbook of Latent Variable and Related Models and author of over 140 papers.