The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Analysis of multivariate probit models
Open accessAbstract
This paper provides a practical simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods and maximum likelihood estimates are obtained by a Monte Carlo version of the EM algorithm. A practical approach for the computation of Bayes factors from the simulation output is also developed. The methods are applied to a dataset with a bivariate binary response, to a four-year longitudinal dataset from the Six Cities study of the health effects of air pollution and to a sevenvariate binary response dataset on the labour supply of married women from the Panel survey of Income Dynamics.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
Cited by
Bayesian Measures of Model Complexity and Fit
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151
Discrete Choice Methods with Simulation
Train, K. E. (2001) · doi:10.1017/cbo9780511805271 · Cited by 7,784
Computationally efficient whole-genome regression for quantitative and binary traits
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00870-7 · Cited by 1,502
blavaan : Bayesian Structural Equation Models via Parameter Expansion
Merkle, E. C., & Rosseel, Y. (2018) · Journal of Statistical Software · doi:10.18637/jss.v085.i04 · Cited by 360
Dominici, F., Samet, J. M., & Zeger, S. L. (2000) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/1467-985x.00170 · Cited by 286
Adhikari, S., & Khanal, A. R. (2021) · Sustainability · doi:10.3390/su13041741 · Cited by 18
Metadata
- Authors
- Chib, S.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1998-06-01
- Volume
- 85
- Issue
- 2
- Pages
- 347-361
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2032113267
- Cited by
- 834
- References
- 3
- FWCI
- 22.34
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
