Analysis of multivariate probit models

Biometrika

Volumn 85, Issue 2, 1998, Pages 347-361

  Chib, Siddhartha ^a,b   Greenberg, Edward ^a  

^a WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^b WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000911137     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/85.2.347     Document Type: Article

References (27)

1. ALBERT, J. & CHID, S. (1993). Bayesian analysis of binary and polychotomous response data. J. Am. Statist. Assoc. 88, 669-79.
2. AMEMIYA, T. ( 1972). Bivariate probit analysis: minimum chi-square methods. J. Am. Statist. Assoc. 69, 940-4.
3. ASHFORD, J. R. & SOWDEN, R. R. (1970). Multivariate probit analysis. Biometrics 26, 535-46.
4. AVERY, R. B., HANSEN, L. P. & HOTZ, V. J. (1983). Multiperiod probit models and orthogonality condition estimation. Int. Econ. Rev. 24, 21-35.
5. CAREY, V., ZEGER, S. L. & DIGGLE, P. (1993). Modelling multivariate binary data with alternating logistic regressions. Biometrika 80, 517-26.
6. CHIB, S. (1995). Marginal likelihood from the Gibbs output. J. Am. Statist. Assoc. 90, 1313-21.
7. CHIB, S. & GREENBERG, E. (1995). Understanding the Metropolis-Hastings algorithm. Am. Statistician 49, 327-35.
8. DEMPSTER, A. P., LAIRD, N. M. & RUBIN, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with Discussion). J. R. Statist. Soc. B 39, 1-38.
9. DEVROYE, L. (1985). Non-Uniform Random Variate Generation. New York: Springer Verlag.
10. FITZMAURICE, G. F. V. & LAIRD, N. M. (1993). A likelihood-based method for analysing longitudinal binary responses. Biometrika 80, 141-51.
11. GELFAND, A. E. & SMITH, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. J. Am. Statist. Assoc. 85, 398-409.
12. GEWEKE, J. (1991). Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints. In Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, Ed. E. Keramidas and S. Kaufman, pp. 571-8. Fairfax Station, VA: Interface Foundation of North America.
13. GLOXEK, G. F. V. & McCuLLAGii, P. (1995). Multivariate logistic models. J. R. Statist. Soc. B 57, 533-46.
14. GREENE, W. (1997). Econometric Analysis, 3rd ed. Upper Saddle River, NJ: Prentice Hall.
15. HASTINGS, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97-109.
16. KASS, R. E. & RAFTERY, A. E. (1995). Bayes factors. J. Am. Statist. Assoc. 90, 773-95.
17. KIEFER, N. M. (1982). Testing for dependence in multivariate probit models. Biometrika 69, 161-6.
18. Louis, T. A. (1982). Finding the observed information matrix using the EM algorithm. J. R. Statist. Soc. B 44, 226-33.
19. MARSAGLIA, G. & OLKIN, I. (1984). Generating correlation matrices. SI AM J. Sci. Statist. Camp. 5, 470-5.
20. McCuLLOCH, R. E. & Rossi, P. E. (1994). Exact likelihood analysis of the multinomial probit model. J. Economet. 64, 207-40.
21. MENG, X. & RUBIN, D. B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 80, 267-78.
22. OCHI, Y. & PRENTICE, R. L. (1984). Likelihood inference in a correlated probit regression model. Biometrika 71, 531-43.
23. ROUSSEEUW, P. & MOLENBERGHS, G. (1994). The shape of correlation matrices. Am. Statistician 48, 276-9.
24. SCOTT, D. W. (1992). Muhivariate Density Estimation. New York: John Wiley.
25. SMITH, A. F. M. & ROBERTS, G. O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. J. R. Statist. Soc. B 55, 3-21.
26. TIERNEY, L. (1994). Markov chains for exploring posterior distributions (with Discussion). Ann. Statist. 22, 1701-62.
27. WEI, G. C. G. & TANNER, M. A. (1990). A Monte Carlo implementation of EM algorithm and the poor man's data augmentation algorithm. J. Am. Statist. Assoc. 85, 699-704.