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Bayesian Analysis of Structural Equation Modeling

  • Kazuo Shigemasu
  • Takahiro Hoshino
  • Takuya Ohmori

Summary

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.

Keywords

Structural Equation Model Posterior Distribution Prior Distribution Factor Score Gibbs Sampler 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Arminger, G., and Muthén, B.O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hasting algorithm. Psychometrika, 63, 271 - 300.CrossRefGoogle Scholar
  2. Chen. M-H., Shao, Q-M., and Ibrahim, J.G. (2000), Monte Carlo methods in Bayesian computation. New York: Springer-Verlag.Google Scholar
  3. 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
  4. Gelman, A., Meng, X.-L., and Stern, H.S. (1996). Posterior predictive assessment of model fitness via realized discrepancies(with discussion). Statistica Sinica, 6, 733 - 807.MathSciNetMATHGoogle Scholar
  5. Geman, S., and Geman, D. (1984). Stochastic relaxation,Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721 - 741.CrossRefMATHGoogle Scholar
  6. 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
  7. Shi, J-Q., and Lee, S-Y. (1998). Bayesian sampling-based approach for factor analysis models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology, 51, 233 - 252.CrossRefGoogle Scholar
  8. Shi, J-Q., and Lee, S-Y., (1997). A Bayesian estimation of factor score in confirmatory factor model with polytomous, censored or truncated data. Psychometrika, 62, 29 - 50.CrossRefMATHGoogle Scholar
  9. 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
  10. Smith,B.J. (2000) Bayesian Output Analysis Program(BOA) Version 0.5.0 User-manual (http://www.public-health.uiowa.edu/BOA). Google Scholar

Copyright information

© Springer Japan 2002

Authors and Affiliations

  • Kazuo Shigemasu
    • 1
  • Takahiro Hoshino
    • 1
  • Takuya Ohmori
    • 1
  1. 1.University of TokyoMegroku, TokyoJapan

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