# Quantile Regression by Dyadic CART

@inproceedings{Padilla2021QuantileRB, title={Quantile Regression by Dyadic CART}, author={Oscar Hernan Madrid Padilla and Sabyasachi Chatterjee}, year={2021} }

In this paper we propose and study a version of the Dyadic Classification and Regression Trees (DCART) estimator from Donoho (1997) for (fixed design) quantile regression in general dimensions. We refer to this proposed estimator as the QDCART estimator. Just like the mean regression version, we show that a) a fast dynamic programming based algorithm with computational complexity O(N logN) exists for computing the QDCART estimator and b) an oracle risk bound (trading off squared error and a…

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