Structural Equation Modelling: A Bayesian Approach (Wiley Series in Probability and... by Sik-Yum Lee

Structural Equation Modelling: A Bayesian Approach (Wiley Series in Probability and...

Features

Cover Type: Hard Cover with 458 pages

Published by: Wiley

Edition: 1st Edition March 23, 2007

Written in: English

ISBN 10 Number: 0470024232

ISBN 13 Number: 978-0470024232

Book Dimensions: 9.2 x 6.3 x 1.2 inches

Weighs: 1.7 pounds

Product Description
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.

Back Cover Copy
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.

Reader Reviews
Bayesian methods are moving into structural equation modeling. The most sophisticated approach to modeling interactions is Bayesian. People who want to be able to predict the values of observed variables need a Bayesian approach. This book, with the code and datasets available from the publisher's website, will help you to estimate SE models using the Bayesian approach and the free WinBUGS software. Yes, it's a math-heavy book, but Sik-Yum Lee does a great job explaining this very different approach. Lee demonstrates Bayesian methods applied to basic models, interaction models, mixture models, multi-level models, and models with non-normal distributions. You really want to have this book, if you are a serious SEM user. Comment | | (Report this)

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Updated on 6-4-2008.