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Bayesian Nonparametric Inference for a Multivariate Copula Function

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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.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China

    Juan Wu

  2. School of Mathematics, Statistics and Actuarial Science, University of Kent at Canterbury, Canterbury, UK

    Xue Wang & Stephen G. Walker

Authors
  1. Juan Wu
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  2. Xue Wang
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  3. Stephen G. Walker
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Correspondence to Juan Wu.

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Wu, J., Wang, X. & Walker, S.G. Bayesian Nonparametric Inference for a Multivariate Copula Function. Methodol Comput Appl Probab 16, 747–763 (2014). https://doi.org/10.1007/s11009-013-9348-5

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  • DOI: https://doi.org/10.1007/s11009-013-9348-5

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