Missing data imputation using the multivariate t distribution

Journal of Multivariate Analysis

Volumn 53, Issue 1, 1995, Pages 139-158

  Liu, Chuanhai ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000106418     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1006/jmva.1995.1029     Document Type: Article

References (23)

1. Anscombe, F. J. (1967). Topics in the investigation of linear relations fitted by the method of least squares. J. Roy. Statist. Soc. Ser. B 29 1 52.
2. Box, G. H. P., and Tiao, G. C. (1973). Bayesian Inference in Statistical Analysis. Addison-Wesley, Reading, MA.
3. Dempster, A. P. Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B 39 1-38.
4. Jeffreys, S. H. (1957). Scientific Inference (2nd ed.) Syndics of the Cambridge Univ. Press, Cambridge.
5. Johnson, N. L. and Kotz, S. (1972). Distribution in Statistics'. Continuous Multivariate Distributions. Wiley. New York.
6. Johnson, M. E. (1987). Multivariate Statistical Simulation. Wiley, New York.
7. Lange, K. L., Little, R. J. A., and Taylor, J. M. G. (1989). Robust statistical modeling using the t distribution. J. Amer. Statist. Assoc. 84 881 896.
8. Little, R. J. A. (1988). Robust estimation of the mean and covariance matrix from data with missing values. Appl. Statist. 37 23-39.
9. Little, R. J. A., and Rubin D. B, (1987). Statistical Analysis with Missing Data. Wiley, New York.
10. Liu C. H. (1993). Bartlett's decomposition of the posterior distribution of the covariance for normal monotone ignorable missing data. J. Multivariate Anal. 46 198-206.
11. Liu C. H., and Rubin, D. B. (1994). The F.CME algorithm: An simple extension of EM and ECM with faster monotone convergence. Biometrika 81, to appear.
12. Liu, C. H and Rubin D. B. (1995). ML estimation of the multivariate t distribution with unknown degrees of freedom Slatistica Sinica, to appear.
13. Liu, J. S., Wong, W. H., and Kong A. (1995). Correlation structure and the convergence of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes. Biometrika 81, to appear.
14. Menu, X. L., and Rubin, D. B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 80 267 278.
15. Relles, D. A., and Rogers, W. H. (1977). Statistics are fairly robust estimators of location. J. Amer. Statist. Assoc. 72 107-111.
16. Ritter, C. and Tanner, M. (1992). Facilitating the Gibbs sampler: The Gibbs stopper and the Griddy-Gibbs sampler. J. Amer. Statist. Assoc. 87 861-868.
17. Rubin D. B. (1976). Inference and missing data. Biometrika 63 581-592.
18. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley, New York.
19. Rubin, D. B., and Schafer, J. L. (1990). Efficiently creating multiple imputations for incomplete multivariate normal data. In Proceedings of the Statistical Computing Section of the American Statistical Association, pp. 83 88. Amer. Statistical Assoc. Washington, DC.
20. Schafer, J. L. (in press). Analysis by Simulation of Iincomplete Multivariate Data: Algorithms and Examples. Chapman and Hall, New York.
21. Tanner, M. A., and Wong W. H. (1987). The calculation of posterior distributions by-data augmentation (with discussion). J. Amer. Statist. Assoc. 82 528 550.
22. Shih, W. J., and Weisberg, S. (1986). Assessing influence in multiple linear regression with incomplete data. Technometrics 28 231-239.
23. Tiao G. C. (1967). Discussion on “Topics in the Investigation of Linear Relations Fitted by the Method of Least Squares." J. Roy. Statist. Soc. Ser. B 29 44-47.