Structural Equation Modelling

A Bayesian Approach

Sik-Yum Lee - Collection Wiley Series in Probability and Statistics

444 pages, parution le 12/02/2007

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Résumé

Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples.

Structural Equation Modeling 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.

Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results.
Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison.
Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations.
Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology.
Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets.

Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

L'auteur - Sik-Yum Lee

Sik-Yum Lee, Department of Statistics, Chinese University of Hong Kong Chair of department. Excellent research record, with over 100 published papers in leading journals.

Sommaire

Introduction.
Some Basic Structural Equation Models.
Covariance Structure Analysis.
Bayesian Estimation of Structural Equation Models.
Model Comparison and Model Checking.
Structural Equation Models with Continuous and Ordered Categorical Variables.
Structural Equation Models with Dichotomous Variables.
Nonlinear Structural Equation Models.
Two-level Nonlinear Structural Equation Models.
Multisample Analysis of Structural Equation Models.
Finite Mixtures in Structural Equation Models.
Structural Equation Models with Missing Data.
Structural Equation Models with Exponential Family of Distributions.
Conclusion.

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Sik-Yum Lee
Collection	Wiley Series in Probability and Statistics
Parution	12/02/2007
Nb. de pages	444
Format	16 x 23,5
Couverture	Relié
Poids	783g
Intérieur	Noir et Blanc
EAN13	9780470024232
ISBN13	978-0-470-02423-2