Prediction based on conditional distributions of vine copulas

TL;DR

This paper introduces a vine copula regression method that models complex multivariate distributions and efficiently computes conditional responses, outperforming linear regression especially with heteroscedastic data.

Contribution

It proposes a novel vine copula regression approach using regular vines for mixed data, enabling flexible modeling of conditional distributions in observational studies.

Findings

Outperforms linear regression in heteroscedastic settings

Effective on simulated and real datasets

Handles mixed continuous and discrete variables

Abstract

Vine copulas are a flexible tool for multivariate non-Gaussian distributions. For data from an observational study where the explanatory variables and response variables are measured together, a proposed vine copula regression method uses regular vines and handles mixed continuous and discrete variables. This method can efficiently compute the conditional distribution of the response variable given the explanatory variables. The performance of the proposed method is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula regression method is superior to linear regression in making inferences with conditional heteroscedasticity.