Efficient Bayesian inference for Gaussian copula regression models

• 成果类型: Article
• 署名作者: Pitt, Michael; Chan, David; Kohn, Robert
• 署名单位: University of Warwick; University of New South Wales Sydney
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.3.537
• 发表日期: 2006
• 页码: 537554
• 关键词: covariance; selection

摘要

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.