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Efficient Bayesian inference for Gaussian copula regression models.
- Source :
-
Biometrika . Sep2006, Vol. 93 Issue 3, p537-554. 18p. 3 Charts, 3 Graphs. - Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00063444
- Volume :
- 93
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Biometrika
- Publication Type :
- Academic Journal
- Accession number :
- 22770381
- Full Text :
- https://doi.org/10.1093/biomet/93.3.537