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- W2044146548 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." @default.
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- W2044146548 date "2013-06-11" @default.
- W2044146548 modified "2023-10-12" @default.
- W2044146548 title "Bayesian nonparametric estimation of a copula" @default.
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- W2044146548 doi "https://doi.org/10.1080/00949655.2013.806508" @default.
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