Adaptive nonparametric confidence sets

dc.creatorRobins, James
dc.creatorvan der Vaart, Aad
dc.date2006-05-17
dc.date.accessioned2026-07-07T08:07:49Z
dc.date.available2026-07-07T08:07:49Z
dc.descriptionWe construct honest confidence regions for a Hilbert space-valued parameter in various statistical models. The confidence sets can be centered at arbitrary adaptive estimators, and have diameter which adapts optimally to a given selection of models. The latter adaptation is necessarily limited in scope. We review the notion of adaptive confidence regions, and relate the optimal rates of the diameter of adaptive confidence regions to the minimax rates for testing and estimation. Applications include the finite normal mean model, the white noise model, density estimation and regression with random design.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053605000000877 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0605473
dc.identifierhttp://arxiv.org/abs/math/0605473
dc.identifierAnnals of Statistics 2006, Vol. 34, No. 1, 229-253
dc.identifierdoi:10.1214/009053605000000877
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131058
dc.subjectStatistics Theory
dc.subject62G15, 62G20, 62F25 (Primary)
dc.titleAdaptive nonparametric confidence sets
dc.typetext

Files

Collections