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Abstract Details
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Activity Number:
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26
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #301006 |
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Title:
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Bayesian Semiparametric Regression via Copulas
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Author(s):
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Ori Rosen*+ and Wesley Thompson
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Companies:
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The University of Texas at El Paso and University of California at San Diego
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Address:
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, , ,
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Keywords:
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Gaussian Copula ;
MCMC ;
Regression
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Abstract:
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We propose a Bayesian semiparametric regression model for continuous data using the Gaussian copula. The association parameters as well as the marginal distributions are estimated simultaneously without making a parametric assumption on the marginal distributions. The conditional mean of the dependent variable conditional on the covariates is also estimated, facilitating a regression analysis. Markov chain Monte Carlo methods are used for the estimation. The method is illustrated with real and simulated data.
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