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Michael Pitt, David Chan, Robert Kohn, Efficient Bayesian inference for Gaussian copula regression models, Biometrika, Volume 93, Issue 3, September 2006, Pages 537–554, https://doi.org/10.1093/biomet/93.3.537
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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.
© 2006 Biometrika Trust
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