# Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas

arXiv: https://arxiv.org/abs/2406.13500 · HTML: https://mlsanity.com/paper/2406.13500/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances](https://mlsanity.com/paper/2506.13682/) (2025)

## Abstract (arXiv)

Vine copulas are flexible dependence models using bivariate copulas as building blocks. If the parameters of the bivariate copulas in the vine copula depend on covariates, one obtains a conditional vine copula. We propose an extension for the estimation of continuous conditional vine copulas, where the parameters of continuous conditional bivariate copulas are estimated sequentially and separately via gradient-boosting. For this purpose, we link covariates via generalized linear models (GLMs) to Kendall's τ correlation coefficient from which the corresponding copula parameter can be obtained. Consequently, the gradient-boosting algorithm estimates the copula parameters providing a natural covariate selection. In a second step, an additional covariate deselection procedure is applied. The performance of the gradient-boosted conditional vine copulas is illustrated in a simulation study. Linear covariate effects in low- and high-dimensional settings are investigated for the conditional bivariate copulas separately and for conditional vine copulas. Moreover, the gradient-boosted conditional vine copulas are applied to the temporal postprocessing of ensemble weather forecasts in a low-dimensional setting. The results show, that our suggested method is able to outperform the benchmark methods and identifies temporal correlations better. Eventually, we provide an R-package called boostCopula for this method.