Bayesian sampling-based approach for factor analysis models with continuous and polytomous data

British Journal of Mathematical and Statistical Psychology

Volumn 51, Issue 2, 1998, Pages 233-252

  Shi, Jian Qing ^a   Lee, Sik Yum ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000592609     PISSN: 00071102     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.2044-8317.1998.tb00679.x     Document Type: Article

References (34)

1. Albert, J. H. & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669-679.
2. Anderson, T. W. & Rubin, H. (1956). Statistical inference in factor analysis. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, pp. 110-150. Berkeley: University of California Press.
3. Arminger, G. & Küsters, U. (1988). Latent trait models with indicators of mixed measurement level. In R. Langeheine and J. Rost (Eds), Latent Trait and Latent Class Models. New York: Plenum.
4. Bentler, P. M. (1983). Some contributions to efficient statistics for structural models: Specification and estimation of moment structures. Psychometrika, 48, 493-517.
5. Bentler, P. M. (1992). EQS: Structural Equation Program Manual. Los Angeles: BMDP Statistical Software.
6. Broemeling, L. D. (1985). Bayesian Analysis of Linear Models. New York: Marcel Dekker.
7. Browne, M. W. (1984). Asymptotically distribution-free methods in the analysis of covariance structure. British Journal of Mathematical and Statistical Psychology, 37, 62-83
8. Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409.
9. Gelman, A. (1996). Inference and monitoring convergence. In W. R. Gilks, S. Richardson and D. J. Speigelhalter (Eds), Markov Chain Monte Carlo in Practice, 131-144. London: Chapman Hall.
10. Gelman, A. & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequence (with discussion). Statistical Science, 7, 457-472.
11. Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
12. Geyer, C. J. (1992). Practical Markov chain Monte Carlo (with discussion). Statistical Science, 7, 473-511
13. Gilks, W. R., Richardson, S. & Spiegelhalter, D. J. (1996). Introducing Markov chain Monte Carlo. In W. R. Gilks, S. Richardson and D. J. Spiegelhalter (Eds), Markov Chain Monte Carlo in Practice, pp. 1-19. London: Chapman & Hall.
14. IMSL (1991). FORTRAN Subroutine for Statistical Analysis. Houston, TX: IMSL, Inc.
15. Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443-477.
16. Jöreskog, K. G. (1994). On the estimation of polychoric correlations and their asymptotic covariance matrix. Psychometrika, 59, 381-389.
17. Jöreskog, K. C. & Sörbom, D. (1996). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Hove and London: Scientific Software International.
18. Lawley, D. N. & Maxwell, A. E. (1971). Factor Analysis as a Statistical Method, 2nd ed. New York: American Elsevier.
19. Lee, S. Y., Poon, W. Y. & Bentler, P. M. (1990). Full maximum likelihood analysis of structural equation models with polytomous variables. Statistics and Probability Letters, 9, 91-97.
20. Lee, S. Y., Poon, W. Y. & Bentler, P. M. (1995). A two-stage estimation of structural equation models with continuous and polytomous variables. British Journal of Mathematical and Statistical Psychology, 48, 339-358.
21. Liu, J. S., Cheng, R. & Wong, W. H. (1996). Rejection control and importance sampling. Technical Report, Harvard University.
22. Meng, X. L. & Schilling, S. (1996). Fitting full-information item factor models and an empirical investigation of bridge sampling. Journal of the American Statistical Association, 91, 1254-1267.
23. Moustaki, I. (1996). A latent trait and a latent class model for mixed observed variables. British Journal of Mathematical and Statistical Psychology, 49, 313-334.
24. Muthén, B. (1987). LISCOMP: Analysis of Linear Statistical Equation with a Comprehensive Measurement Model. Mooresville, IN: Scientific Software Inc.
25. Newcomb, M. D. & Bentler. P. M. (1988). Consequences of Adolescent Drug Use: Impact on the Lives of Young Adults. Newbury Park, CA: Sage.
26. Poon, W. Y. & Lee, S. Y. (1987). Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients. Psychometrika, 52, 409-430.
27. Raftery, A. E. & Lewis. S. M. (1992). How many iterations of the Gibbs sampler? In J. M. Bernardo, J. O. Berger, A. P. David & A. F. M. Smith (Eds), Bayesian Statistics 4, 765-776. Oxford: Oxford University Press.
28. Raftery, A. E. & Lewis, S. M. (1996). Implementing MCMC. In W. R. Gilks, S. Richardson and D. J. Spiegelhalter (Eds), Markov Chain Monte Carlo in Practice, 115-130. London: Chapman & Hall.
29. Rubin, D. B. (1987). A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: the SIR algorithm, comment on Tanner and Wong (1987). Journal of the American Statistical Association, 52, 543-546.
30. Sammel, M. D., Ryan, L. M. & Legler, J. M. (1997). Latent variable models for mixed discrete and continuous outcomes. Journal of the Royal Statistical Society, Series B, 59, 667-678.
31. Shi, J. Q. & Lee, S. Y. (1997). A Bayesian estimation of factor score in confirmatory factor model with polytomous, censored or truncated data. Psychometrika, 62, 29-50.
32. Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation (with discussion). Journal of American Statistical Association, 82, 528-550.
33. Zeger, S. L. & Karim, M. R. (1991). Generalized linear models with random effects: A Gibbs sampling approach. Journal of American Statistical Association, 86, 79-86.
34. Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. New York: Wiley.