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• W2963253792 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." @default.
• W2963253792 created "2019-07-30" @default.
• W2963253792 creator A5040254438 @default.
• W2963253792 creator A5054046209 @default.
• W2963253792 date "2017-12-01" @default.
• W2963253792 modified "2023-09-24" @default.
• W2963253792 title "Approximate Bayesian Inference in Semiparametric Copula Models" @default.
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• W2963253792 doi "https://doi.org/10.1214/17-ba1080" @default.
• W2963253792 hasPublicationYear "2017" @default.
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