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Efficient Bayesian inference for Gaussian copula regression models

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  • Michael Pitt
  • David Chan
  • Robert Kohn
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.

Suggested Citation

  • Michael Pitt & David Chan & Robert Kohn, 2006. "Efficient Bayesian inference for Gaussian copula regression models," Biometrika, Biometrika Trust, vol. 93(3), pages 537-554, September.
    • Handle: RePEc:oup:biomet:v:93:y:2006:i:3:p:537-554
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    File URL: http://hdl.handle.net/10.1093/biomet/93.3.537
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