Quantile Regression Tree

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.