Implicit Copulas from Bayesian Regularized Regression Smoothers

TL;DR

This paper introduces a method to extract and evaluate implicit copulas from Bayesian regularized regression smoothers, enabling flexible modeling of complex dependence structures in multivariate responses.

Contribution

It presents a novel approach to derive and compute implicit copulas from Bayesian smoothers with various shrinkage priors, enhancing dependence modeling capabilities.

Findings

Implicit copulas are high-dimensional with flexible dependence structures.

The method allows evaluation via Gaussian copula conditioning and integration.

Copula smoothing improves function estimates and predictive distributions.

Abstract

We show how to extract the implicit copula of a response vector from a Bayesian regularized regression smoother with Gaussian disturbances. The copula can be used to compare smoothers that employ different shrinkage priors and function bases. We illustrate with three popular choices of shrinkage priors --- a pairwise prior, the horseshoe prior and a g prior augmented with a point mass as employed for Bayesian variable selection --- and both univariate and multivariate function bases. The implicit copulas are high-dimensional, have flexible dependence structures that are far from that of a Gaussian copula, and are unavailable in closed form. However, we show how they can be evaluated by first constructing a Gaussian copula conditional on the regularization parameters, and then integrating over these. Combined with non-parametric margins the regularized smoothers can be used to model the distribution of non-Gaussian univariate responses conditional on the covariates. Efficient Markov chain Monte Carlo schemes for evaluating the copula are given for this case. Using both simulated and real data, we show how such copula smoothing models can improve the quality of resulting function estimates and predictive distributions.