Adaptive nonparametric estimation in heteroscedastic regression models. Part 1: Sharp non-asymptotic Oracle inequalities

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Galtchouk, Leonid
Pergamenshchikov, Serguey
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An adaptive nonparametric estimation procedure is constructed for the estimation problem of heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound for a quadratic risk (an oracle inequality) is constructed.
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Mathematics - Statistics Theory, 62G08, 62G05, 62G20
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