Quantile Regression Tree

Jaachinma Okafor; Lateefah Isegen; Ark Ifeanyi

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arXiv · 2402.14955

Quantile Regression Tree

Jaachinma Okafor·Lateefah Isegen·Ark Ifeanyi

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Abstract

This study introduces and evaluates the Quantile Regressor Tree (QRT), a novel methodology merging the robust characteristics of quantile regression with the versatility of decision trees. The quantile regressor tree introduces non-linearity to the quantile regression due to the splitting by features in the decision tree, enhancing flexibility while maintaining interpretability. The quantile regression tree gives a parametric and non-parametric mixture of estimating conditional quantiles for high-dimensional predictor variables.

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Jaachinma Okafor, Lateefah Isegen, Ark Ifeanyi. 2024-02-22. Quantile Regression Tree. https://arxiv.org/abs/2402.14955

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