5. Practical Structural Equation Models

  1. Xin-Yuan Song and
  2. Sik-Yum Lee

Published Online: 18 JUL 2012

DOI: 10.1002/9781118358887.ch5

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

How to Cite

Song, X.-Y. and Lee, S.-Y. (2012) Practical Structural Equation Models, in Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118358887.ch5

Author Information

  1. Department of Statistics, The Chinese University of Hong Kong

Publication History

  1. Published Online: 18 JUL 2012
  2. Published Print: 24 AUG 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470669525

Online ISBN: 9781118358887

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Keywords:

  • Bayesian analysis;
  • exponential family distribution;
  • Markov chain Monte Carlo (MCMC);
  • structural equation models (SEMs)

Summary

This chapter introduces some generalizations of the standard structural equation models (SEMs) for analyzing complex data sets. These include SEMs with mixed continuous and ordered categorical variables, SEMs with variables coming from an exponential family distribution, and SEMs with missing data. It illustrates how the Bayesian methodologies can be naturally extended to these generalizations, through data augmentation and Markov chain Monte Carlo (MCMC) techniques.

Controlled Vocabulary Terms

Bayes estimator; Bayesian inference; Exponential family distribution; Markov chain Monte Carlo estimation