Boosting with copula-based components
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 31 references that are in the map. First posted on arXiv 9 Aug 2022.
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7 of 31 references.
References · 7
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2014Deep Learning in Neural Networks: An Overviewcited by 765
- 2018Prediction based on conditional distributions of vine copulascited by 8
- 2016Generalized Additive Models for Pair-Copula Constructionscited by 4
- 2017A generic approach to nonparametric function estimation with mixed datacited by 4
- 2017D-vine quantile regression with discrete variablescited by 4
- 2020An information criterion for automatic gradient tree boostingcited by 3
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