# Bayesian Nonparametric Inference for a Multivariate Copula Function

Wu, Juan, Wang, Xue, Walker, Stephen G. (2014) Bayesian Nonparametric Inference for a Multivariate Copula Function. Methodology and Computing in Applied Probability, 16 (3). pp. 747-763. ISSN 1387-5841. (doi:10.1007/s11009-013-9348-5)

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http://dx.doi.org/10.1007/s11009-013-9348-5

## 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. Consistency of the Bayesian nonparametric model is established.

Item Type: Article 10.1007/s11009-013-9348-5 Bayesian nonparametric estimation; copula; infinite mixture skewnormal copula model; Metropolis{Hastings algorithm. Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics Xue Wang 10 Dec 2013 10:21 UTC 29 May 2019 11:37 UTC https://kar.kent.ac.uk/id/eprint/37428 (The current URI for this page, for reference purposes)
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