The Library
Efficient Bayesian inference for Gaussian copula regression models
Tools
Tools
Tools
Pitt, Michael, Chan, David and Kohn, Robert. (2006) Efficient Bayesian inference for Gaussian copula regression models. BIOMETRIKA, 93 (3). pp. 537-554. ISSN 0006-3444
Full text not available from this repository.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.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QH Natural history > QH301 Biology Q Science > QA Mathematics |
| Journal or Publication Title: | BIOMETRIKA |
| Publisher: | OXFORD UNIV PRESS |
| ISSN: | 0006-3444 |
| Date: | September 2006 |
| Volume: | 93 |
| Number: | 3 |
| Number of Pages: | 18 |
| Page Range: | pp. 537-554 |
| Identification Number: | 10.1093/biomet/93.3.537 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/32947 |
Data sourced from Thomson Reuters' Web of Knowledge
Actions (login required)
 |
View Item |
