Additive Models for Conditional Copulas

TL;DR

This paper introduces additive models for conditional copulas in multivariate regression, providing Bayesian inference methods, and demonstrates their effectiveness through simulations and real data analysis.

Contribution

It proposes a novel additive modeling approach for conditional copulas with Bayesian inference, enhancing flexibility in multivariate regression analysis.

Findings

Effective modeling of joint distributions with multiple covariates

Bayesian inference methods for additive copula models

Successful application to simulated and real datasets

Abstract

Conditional copulas are flexible statistical tools that couple joint conditional and marginal conditional distributions. In a linear regression setting with more than one covariate and two dependent outcomes, we propose the use of additive models for conditional bivariate copula models and discuss computation and model selection tools for performing Bayesian inference. The method is illustrated using simulations and a real example.