Bayesian Analysis of Structural Equation Modeling
A Bayesian procedure to make exact distributional inferences about all structural parameters and latent variables was proposed. This procedure handles the problem associated with the fixed parameters by means of conditinalization, and uses the Gibbs sampler to derive the posterior distribution for each unknown quantitiy. A simulation study was conducted to evaluate the performance of the proposed procedure.
KeywordsStructural Equation Model Posterior Distribution Prior Distribution Factor Score Gibbs Sampler
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- Chen. M-H., Shao, Q-M., and Ibrahim, J.G. (2000), Monte Carlo methods in Bayesian computation. New York: Springer-Verlag.Google Scholar
- Gelfand, A.E., and Smith, A.F.M. (1990), Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association, 85, 398-409. Gelman, A., and Rubin, D.B. (1992), Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 7, 457-511.Google Scholar
- Press, S.J. (1982), Applied multivariate analysis. Florida: Krieger Publishing. Scheines, R., Hoijtink, H., and Boomsma, A. (1999). Bayesian estimation and testing of structural equation models. Psychometrika, 64, 37-52.Google Scholar
- Shi, J-Q., and Lee, S-Y., (2000). Latent variable models with mixed continuous and polytomous data. Journal of the Royal statistical Society, Series B, 62, 77-87. Shigemasu,K and Nakamura,T(1993) A Bayesian Numerical Estimation Procedure in Factor Analysis Model. E.S.T. Research Report, 93-6, Tokyo Institute of Technology.Google Scholar
- Smith,B.J. (2000) Bayesian Output Analysis Program(BOA) Version 0.5.0 User-manual (http://www.public-health.uiowa.edu/BOA). Google Scholar