Bayesian estimation of a bivariate copula using the Jeffreys prior

TL;DR

This paper develops a Bayesian method for estimating bivariate copulas using a Jeffreys prior, providing a proper prior and demonstrating improved estimation accuracy over traditional methods through simulations.

Contribution

It derives a proper Jeffreys prior for the space of doubly stochastic matrices and applies Bayesian estimation to copulas, which is novel and computationally feasible.

Findings

Jeffreys prior is proper and explicitly derived.

Bayesian estimator outperforms classical estimators in simulations.

Method shows promise for accurate copula estimation.

Abstract

A bivariate distribution with continuous margins can be uniquely decomposed via a copula and its marginal distributions. We consider the problem of estimating the copula function and adopt a Bayesian approach. On the space of copula functions, we construct a finite-dimensional approximation subspace that is parametrized by a doubly stochastic matrix. A major problem here is the selection of a prior distribution on the space of doubly stochastic matrices also known as the Birkhoff polytope. The main contributions of this paper are the derivation of a simple formula for the Jeffreys prior and showing that it is proper. It is known in the literature that for a complex problem like the one treated here, the above results are difficult to obtain. The Bayes estimator resulting from the Jeffreys prior is then evaluated numerically via Markov chain Monte Carlo methodology. A rather extensive simulation experiment is carried out. In many cases, the results favour the Bayes estimator over frequentist estimators such as the standard kernel estimator and Deheuvels' estimator in terms of mean integrated squared error.