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
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