Bivariate vine copula based regression, bivariate level and quantile curves

TL;DR

This paper develops a novel vine copula-based regression method for modeling bivariate quantiles and level curves, addressing limitations of traditional univariate approaches and enabling flexible, joint analysis of two responses.

Contribution

It introduces a new graph structure model for symmetric bivariate regression using vine copulas, ensuring computational tractability and avoiding common regression issues.

Findings

Demonstrates flexible bivariate quantile curves with vine copulas.

Shows improved modeling of joint responses over separate univariate regressions.

Applied successfully to weather data from Seoul, Korea.

Abstract

The statistical analysis of univariate quantiles is a well developed research topic. However, there is a need for research in multivariate quantiles. We construct bivariate (conditional) quantiles using the level curves of vine copula based bivariate regression model. Vine copulas are graph theoretical models identified by a sequence of linked trees, which allow for separate modelling of marginal distributions and the dependence structure. We introduce a novel graph structure model (given by a tree sequence) specifically designed for a symmetric treatment of two responses in a predictive regression setting. We establish computational tractability of the model and a straight forward way of obtaining different conditional distributions. Using vine copulas the typical shortfalls of regression, as the need for transformations or interactions of predictors, collinearity or quantile crossings are avoided. We illustrate the copula based bivariate level curves for different copula distributions and show how they can be adjusted to form valid quantile curves. We apply our approach to weather measurements from Seoul, Korea. This data example emphasizes the benefits of the joint bivariate response modelling in contrast to two separate univariate regressions or by assuming conditional independence, for bivariate response data set in the presence of conditional dependence.