Bayesian Analysis

Approximate Bayesian Inference in Semiparametric Copula Models

Clara Grazian and Brunero Liseo

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Abstract

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.

Article information

Source
Bayesian Anal. Volume 12, Number 4 (2017), 991-1016.

Dates
First available in Project Euclid: 8 November 2017

Permanent link to this document
https://projecteuclid.org/euclid.ba/1510110045

Digital Object Identifier
doi:10.1214/17-BA1080

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

Grazian, Clara; Liseo, Brunero. Approximate Bayesian Inference in Semiparametric Copula Models. Bayesian Anal. 12 (2017), no. 4, 991--1016. doi:10.1214/17-BA1080. https://projecteuclid.org/euclid.ba/1510110045


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