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.

Approximate Bayesian inference in semiparametric copula models / Liseo, Brunero; Grazian, Clara. - In: BAYESIAN ANALYSIS. - ISSN 1936-0975. - STAMPA. - 12:4(2017), pp. 991-1016. [10.1214/17-BA1080]

Approximate Bayesian inference in semiparametric copula models

Liseo Brunero;
2017

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1018071
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