Full maximum likelihood estimation of polychoric and polyserial correlations with missing data

Multivariate Behavioral Research

Volumn 38, Issue 1, 2003, Pages 57-79

  Song, Xin Yuan ^a,b   Lee, Sik Yum ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b SUN YAT SEN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0037279421     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1207/S15327906MBR3801_3     Document Type: Article

References (33)

1. Anderson, T. W. (1958). An introduction to multivariate statistical analysis. New York: Wiley.
2. Batholomew, D. J. & Knott, M. (1999). Latent variable models and factor analysis (2nd ed.). London: Griffin.
3. Cox, N. R. (1974). Estimation of the correlation between a continuous and a discrete variable. Biometrics, 30, 171-178.
4. Dempster, A. P., Laird, N. M. & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1-38.
5. Efron, B. (1994). Missing data, imputation and the bootstrap (with discussion). Journal of the American Statistical Association, 89, 463-479.
6. 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.
7. Geweke, J. (1991). Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints. In E. Keramidas & Kanfman (Eds.), Computing science and statistics: Proceedings of the 23rd symposium on the interface, (pp. 571-578). Fairfax Station, VA: Interface Foundation of North America.
8. Gross, A. L. (2000). Bayesian interval estimation of multiple correlations with missing data: A Gibbs sampling approach. Multivariate Behavioral Research, 35, 201-227.
9. Lee, S. Y. & Leung, K. M. (1992). Estimation of multivariate polychoric and polyserial correlation with missing observations. British Journal of Mathematical and Statistical Psychology, 45, 225-238.
10. Lee, S. Y. & Poon, W. Y. (1986). Maximum likelihood estimation of polyserial correlations. Psychometrika, 51, 113-121.
11. 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.
12. Lee, S. Y. & Shi, J. Q. (2001). Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data. Biometrics, 57, 787-794.
13. Lee, S. Y. & Song, X. Y. (2001). Hypothesis testing and model comparison in two-level structural equation models. Multivariate Behavioral Research, 36(4), 639-655.
14. Lee, S. Y. & Zhu, H. T. (2000). Statistical analysis of nonlinear structural equation models with continuous and polytomous data. British Journal of mathematical and Statistical Psychology, 53, 209-232.
15. Little, R. J. A. & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
16. Louis, T. A. (1982). Finding the observed information matrix using EM algorithm. Journal of the Royal Statistical Society, Series B, 44, 226-233.
17. 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.
18. Meng, X. L. & Wong, H. W. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica, 6, 831-360.
19. Morisky, D. E., Tiglao, T. V., Sneed, C. D., Tempongko, S. B., Baltazar, J. C., Detels, R. & Stein, J. A. (1998). The effects of establishment practices, knowledge and attitudes on condom use among Filipina sex workers. AIDS Care, 10, 213-320.
20. Moustaki, I. (2000). A latent variable model for ordinal variables. Applied Psychological Measurement, 24, 211-223.
21. Muthén, B. (1987). LISCOMP: Analysis of linear statistical equation with a comprehensive measurement model. Mooresville, IN: Scientific Software Inc.
22. Olsson, U. (1979a). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44, 443-460.
23. Olsson, U. (1979b). On the robustness of factor analysis against crude classification of the observations. Multivariate Behavioral Research, 14, 485-500.
24. Poon, W. Y. & Lee, S. Y. (1987). Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients. Psychometrika, 52, 409-430.
25. Prince, B. M. & Tate, R. F. (1966). Accuracy of maximum-likelihood estimates of correlation for a biserial model. Psychometrika, 31, 85-92.
26. Roberts, C. P. (1995). Simulation of truncated normal variables. Statistics and Computing, 5, 121-125.
27. 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.
28. Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall/CRC.
29. Scheines, R., Hoijtink, H. & Boomsma, A. (1999). Bayesian estimation and testing of structural equation models. Psychometrika, 64, 37-52.
30. Shi, J. Q. & Lee, S. Y. (2000). Latent variable models with mixed continuous and polytomous data. Journal of the Royal Statistical Society, Series B, 62, 77-87.
31. Song, X. Y. & Lee, S. Y. (2001a). Bayesian estimation and model selection of multivariate linear model with polytomous variables. Multivariate Behavioral Research, 37(4), 453-477.
32. Song, X. Y. & Lee, S. Y. (2001b). Bayesian estimation and test for factor analysis model with continuous and polytomous data in several populations. British Journal of Mathematical and Statistical Psychology, 54, 237-263.
33. Wei, G. C. G. & Tanner, M. A. (1990). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithm. Journal of the American Statistical Association, 85, 699-704.