Bayesian Nonparametric Inference for a Multivariate Copula Function

Juan Wu (), Xue Wang and Stephen G. Walker
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Juan Wu: Huazhong University of Science and Technology
Xue Wang: University of Kent at Canterbury
Stephen G. Walker: University of Kent at Canterbury

Methodology and Computing in Applied Probability, 2014, vol. 16, issue 3, 747-763

Abstract: Abstract The paper presents a general Bayesian nonparametric approach for estimating a high dimensional copula. We first introduce the skew–normal copula, which we then extend to an infinite mixture model. The skew–normal copula fixes some limitations in the Gaussian copula. An MCMC algorithm is developed to draw samples from the correct posterior distribution and the model is investigated using both simulated and real applications.

Keywords: Bayesian nonparametric estimation; Copula; Infinite mixture skew–normal copula model; Metropolis–Hastings algorithm; 62H12 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11009-013-9348-5

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