Bayesian nonparametric estimation of a copula

Wu, Juan and Wang, Xue and Walker, Stephen G. (2013) Bayesian nonparametric estimation of a copula. Journal of Statistical Computation and Simulation, N/A (N/A). pp. 1-14. ISSN 0094-9655. (doi:https://doi.org/10.1080/00949655.2013.806508) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

A copula can fully characterize the dependence of multiple variables. The purpose of this paper is to provide a Bayesian nonparametric approach to the estimation of a copula, and we do this by mixing over a class of parametric copulas. In particular, we show that any bivariate copula density can be arbitrarily accurately approximated by an infinite mixture of Gaussian copula density functions. The model can be estimated by Markov chain Monte Carlo methods and the model is demonstrated on both simulated and real data sets.

Item Type: Article
Uncontrolled keywords: Bayesian nonparametric estimation; copula; Gaussian copula; Gibbs sampling; slice sampling.
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 10:07 UTC
Last Modified: 26 Apr 2018 10:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37210 (The current URI for this page, for reference purposes)
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