Bayesian Nonparametric Inference for a Multivariate Copula Function

Wu, Juan and Wang, Xue and 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:https://doi.org/10.1007/s11009-013-9348-5) (Full text available)

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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
Uncontrolled keywords: Bayesian nonparametric estimation; copula; infinite mixture skewnormal copula model; Metropolis{Hastings algorithm.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Xue Wang
Date Deposited: 10 Dec 2013 10:21 UTC
Last Modified: 22 Jan 2015 15:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37428 (The current URI for this page, for reference purposes)
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