Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models
Statistics Theory
2010-02-09 v1 Statistics Theory
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
Cite
@article{arxiv.1002.1538,
title = {Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models},
author = {Leonid Galtchouk and Serguei Pergamenchtchikov},
journal= {arXiv preprint arXiv:1002.1538},
year = {2010}
}