Approximate Bayesian inference in semiparametric copula models
Methodology
2017-07-18 v4 Computation
Abstract
We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution and it is based on the properties of an Approximate Bayesian Monte Carlo algorithm, where the proposed values of the functional of interest are weighed in terms of their 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.
Cite
@article{arxiv.1503.02912,
title = {Approximate Bayesian inference in semiparametric copula models},
author = {Clara Grazian and Brunero Liseo},
journal= {arXiv preprint arXiv:1503.02912},
year = {2017}
}
Comments
27 pages, 18 figures