Open Access
December 2017 Approximate Bayesian Inference in Semiparametric Copula Models
Clara Grazian, Brunero Liseo
Bayesian Anal. 12(4): 991-1016 (December 2017). DOI: 10.1214/17-BA1080


We describe a simple method for making inference on a functional of a multivariate distribution, based on its copula representation. We make use of an approximate Bayesian Monte Carlo algorithm, where the proposed values of the functional of interest are weighted in terms of their Bayesian exponentially tilted empirical likelihood. This method is particularly useful when the “true” likelihood function associated with the working model is too costly to evaluate or when the working model is only partially specified.


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Clara Grazian. Brunero Liseo. "Approximate Bayesian Inference in Semiparametric Copula Models." Bayesian Anal. 12 (4) 991 - 1016, December 2017.


Published: December 2017
First available in Project Euclid: 8 November 2017

zbMATH: 1384.62167
MathSciNet: MR3724976
Digital Object Identifier: 10.1214/17-BA1080

Keywords: Bayesian exponentially tilted empirical likelihood , multivariate dependence , partially specified models , Spearman’s ρ , tail dependence coefficients

Vol.12 • No. 4 • December 2017
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