Adaptive nonparametric estimation in heteroscedastic regression models. Part 1: Sharp non-asymptotic Oracle inequalities
Statistics Theory
2008-12-18 v1 Statistics Theory
Abstract
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.
Cite
@article{arxiv.0804.1716,
title = {Adaptive nonparametric estimation in heteroscedastic regression models. Part 1: Sharp non-asymptotic Oracle inequalities},
author = {Leonid Galtchouk and Serguey Pergamenshchikov},
journal= {arXiv preprint arXiv:0804.1716},
year = {2008}
}