Discussion

Journal of Computational and Graphical Statistics

Volumn 10, Issue 1, 2001, Pages 75-81

  Liu, Chuanhai ^a  

^a LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85011463052     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1198/10618600152418746     Document Type: Article

References (22)

1. Albert, J. H., and Chib, S. (1993), “Bayesian Analysis of Binary and Polychotomous Response Data,” Journal of the American Statistical association, 88, 669-679.
2. Amemiya, T. (1974), “Bivariate Probit Analysis: Minimum Chi-Square Methods,” Journal of the American Sta tisticalAssociation, 69, 940-944.
3. Ashford, J. R., and Sowden, R. R. (1970), “Multi-Variate Probit Analysis,” Biometrics, 26, 535-546.
4. Barnard, J., McCulloch, R., and Meng, X. L. (2000), “Modeling Covariance Matrices in Terms of Standard Deviations and Correlations, With Application to Shrinkage,” Statistica Sinica, 10, 1281-1311.
5. Bliss, C. J. (1935), “The Calculation of the Dosage-Mortality Curve,” Annals ofAppliedBiology, 22, 307-330.
6. Box, G. E. P., and Tiao, G. C. (1973), Bayesian Inference in StatisticalAnalysis, New York: Wiley.
7. Chambers, J. (1999), “ComputingWith Data: Concepts and Challenges,” The American Statistician, 53, 73-84.
8. Chambers, J. (2000). “Users, Programmers, and Statistical Software,” Journal ofComputationaland Graphical Statis tics, 9, 404-422.
9. Chambers, J., and Temple Lang, D. (1999), “Omegahat-A Component-Based Statistical Computing Environ ment,” in Proceedings of the 52nd Session of the ISI, Bulletin of the International Statistical Institute, Tome LVIII, Book 2, Helsinki, Finland, pp. 125-128.
10. Chen, M. H., andLiu, C. (1999), Comments on “SimulatedSintering:Markov ChainMonteCarloWithSpaces of Varying Dimensions” by J. S. Liu and C. Sabatti, in Bayesian Statistics 6, eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, New York: Oxford University Press.
11. Chib, S., and Greenberg, E. (1998), “Analysis of MultivariateProbit Models,” Biometrika, 85, 347-361.
12. Grizzle, J. E. (1971), “MultivariateLogit Analysis,” Biometrics, 27, 1057-1062.
13. Liu, C. (1999), “Covariance Adjustment for Markov Chain Monte Carlo-A General Framework,” Technical Report, Bell Labs. under revision.
14. Liu, C. (2000), “Robit Regression: A Simple Robust Alternative to Logistic and Probit Regression,” Technical Report, Bell Labs.
15. Liu, C., Rubin, D. B., and Wu, Y. (1998), “Parameter Expansion to Accelerate EM: The PX-EM Algorithm,” Biometrika, 85, 755-770.
16. Liu, C., and Sun D. X. (2000), “Analysis of Interval Censored Data From Fractionated Experiments Using Co variance Adjustments,” Technometrics, 42, 353-365.
17. Liu, J. S., and Wu, Y. (1999), “Parameter Expansion Scheme for Data Augmentation,” Journal of the American Statistical association, 94, 1264-1274.
18. Mudholkar, G. S., and George E. O. (1978), “A Remark on the Shape of the Logistic Distribution,” Biometrika, 65, 667-668.
19. Pinheiro J. C., Liu, C., and Wu, Y. N. (in press), “Efficient Algorithms for Robust Estimation in Linear Mixed- Effects Models Using the Multivariate t-Distribution,” Journal ofComputationaland GraphicalStatistics.
20. Rubin, D. B. (1997), Comments on “The EM Algorithm-An Old Folk Song Sung to Fast New Tune” by X. L. Meng and van Dyk, Journal of the Royal StatisticalSociety, Ser. B, 59, 541-542.
21. Temple Lang, D. (2000), “The Omega Project: New Possibilities for Statistical Software,” Journal of Computa tional and Graphical Statistics, 9, 423-451.
22. van Dyk, D., and Meng, X. L. (2001), “The Art of Data Augmentation,” JournalofComputationaland Graphical Statistics, 10, 1-50