The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficient Bayesian inference for Gaussian copula regression models
Abstract
A Gaussian copula regression model gives a tractable way of handling a multivariate regression when some of the marginal distributions are non-Gaussian. Our paper presents a general Bayesian approach for estimating a Gaussian copula model that can handle any combination of discrete and continuous marginals, and generalises Gaussian graphical models to the Gaussian copula framework. Posterior inference is carried out using a novel and efficient simulation method. The methods in the paper are applied to simulated and real data.
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Cites
CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK*
Sharpe, W. F. (1964) · The Journal of Finance · doi:10.1111/j.1540-6261.1964.tb02865.x · Cited by 19,441
Cited by
Extending the rank likelihood for semiparametric copula estimation
D. Hoff, P. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas107 · Cited by 297
Metadata
- Authors
- Pitt, M., Chan, D., & Kohn, R.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-09-01
- Volume
- 93
- Issue
- 3
- Pages
- 537-554
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2022034627
- Cited by
- 256
- References
- 1
- FWCI
- 11.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pitt, M., Chan, D., & Kohn, R. (2006). Efficient Bayesian inference for Gaussian copula regression models. Biometrika. https://doi.org/10.1093/biomet/93.3.537
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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.
