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.

翻译：本研究提出并评估了一种新颖的分位数回归树（QRT）方法，该方法融合了分位数回归的稳健特性与决策树的灵活泛化能力。分位数回归树通过决策树中特征的递归分割引入非线性机制，在增强模型灵活性的同时保持可解释性。该模型采用参数化与非参数化混合估计策略，有效处理高维预测变量条件下的条件分位数估计问题。