Adaptive asymptotically efficient estimation in heteroscedastic nonparametric regression via model selection

Simple item page
dc.creatorGaltchouk, Leonid
dc.creatorPergamenshchikov, Serguey
dc.date2008-10-07
dc.date.accessioned2026-07-07T10:08:06Z
dc.date.available2026-07-07T10:08:06Z
dc.descriptionThe paper deals with asymptotic properties of the adaptive procedure proposed in the author paper, 2007, for estimating a unknown nonparametric regression. We prove that this procedure is asymptotically efficient for a quadratic risk, i.e. the asymptotic quadratic risk for this procedure coincides with the Pinsker constant which gives a sharp lower bound for the quadratic risk over all possible estimators.
dc.identifierhttps://arxiv.org/abs/0810.1173
dc.identifierhttp://arxiv.org/abs/0810.1173
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170877
dc.subjectStatistics Theory
dc.titleAdaptive asymptotically efficient estimation in heteroscedastic nonparametric regression via model selection
dc.typetext

Files

Collections

arXiv