# Prediction based on conditional distributions of vine copulas

arXiv: https://arxiv.org/abs/1807.08429 · HTML: https://mlsanity.com/paper/1807.08429/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features](https://mlsanity.com/paper/2111.13507/) (2021)
- [Explaining predictive models using Shapley values and non-parametric vine copulas](https://mlsanity.com/paper/2102.06416/) (2021)
- [Vine Copulas as Differentiable Computational Graphs](https://mlsanity.com/paper/2506.13318/) (2025)
- [Bivariate vine copula based regression, bivariate level and quantile curves](https://mlsanity.com/paper/2205.02557/) (2022)
- [Throwing Vines at the Wall: Structure Learning via Random Search](https://mlsanity.com/paper/2510.20035/) (2025)
- [Boosting with copula-based components](https://mlsanity.com/paper/2208.04669/) (2022)
- [Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria](https://mlsanity.com/paper/2310.10324/) (2023)

## Abstract (arXiv)

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.