  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.
