ERM Scheme for Quantile Regression

Abstract

This paper considers the ERM scheme for quantile regression. We conduct error analysis for this learning algorithm by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The learning rates are derived by applying concentration techniques involving the ${\ell }^2$-empirical covering numbers.