Generalised bayesian sample copula of order $m$

TL;DR

This paper introduces a semiparametric bivariate copula model with a Bayesian framework, employing spatial priors and advanced inference techniques, demonstrated through simulations and real data analysis.

Contribution

It develops a novel spatially dependent prior for copula parameters and provides a comprehensive Bayesian inference method including a new adaptation scheme.

Findings

Model reduces to sample copula under certain conditions

Bayesian approach yields smooth posterior estimates

Simulation and real data show effective performance

Abstract

In this work we propose a semiparametric bivariate copula whose density is defined by a piecewise constant function on disjoint squares. We obtain the maximum likelihood estimators of model parameters and prove that they reduce to the sample copula under specific conditions. We further propose to carry out a full Bayesian analysis of the model and introduce a spatial dependent prior distribution for the model parameters. This prior allows the parameters to borrow strength across neighbouring regions to produce smooth posterior estimates. To characterise the posterior distribution, via the full conditional distributions, we propose a data augmentation technique. A Metropolis-Hastings step is required and we propose a novel adaptation scheme for the random walk proposal distribution. We implement a simulation study and an analysis of a real dataset to illustrate the performance of our model and inference algorithms.