Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models

Abstract

An adaptive nonparametric estimation procedure is constructed for heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound for a quadratic risk (oracle inequality) is obtained