Efficient Bayesian inference for Gaussian copula regression models

Michael Pitt, David Chan and Robert Kohn ()

Biometrika, 2006, vol. 93, issue 3, 537-554

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. Copyright 2006, Oxford University Press.

Date: 2006
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