# Boosting with copula-based components

arXiv: https://arxiv.org/abs/2208.04669 · HTML: https://mlsanity.com/paper/2208.04669/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Prediction based on conditional distributions of vine copulas](https://mlsanity.com/paper/1807.08429/) (2018)
- [Generalized Additive Models for Pair-Copula Constructions](https://mlsanity.com/paper/1608.01593/) (2016)
- [A generic approach to nonparametric function estimation with mixed data](https://mlsanity.com/paper/1704.07457/) (2017)
- [D-vine quantile regression with discrete variables](https://mlsanity.com/paper/1705.08310/) (2017)
- [An information criterion for automatic gradient tree boosting](https://mlsanity.com/paper/2008.05926/) (2020)

## Abstract (arXiv)

The authors propose new additive models for binary outcomes, where the components are copula-based regression models (Noh et al, 2013), and designed such that the model may capture potentially complex interaction effects. The models do not require discretisation of continuous covariates, and are therefore suitable for problems with many such covariates. A fitting algorithm, and efficient procedures for model selection and evaluation of the components are described. Software is provided in the R-package copulaboost. Simulations and illustrations on data sets indicate that the method's predictive performance is either better than or comparable to the other methods.