A universal procedure for aggregating estimators

dc.creatorGoldenshluger, Alexander
dc.date2007-04-19
dc.date2009-03-04
dc.date.accessioned2026-07-07T12:48:17Z
dc.date.available2026-07-07T12:48:17Z
dc.descriptionIn this paper we study the aggregation problem that can be formulated as follows. Assume that we have a family of estimators $\mathcal{F}$ built on the basis of available observations. The goal is to construct a new estimator whose risk is as close as possible to that of the best estimator in the family. We propose a general aggregation scheme that is universal in the following sense: it applies for families of arbitrary estimators and a wide variety of models and global risk measures. The procedure is based on comparison of empirical estimates of certain linear functionals with estimates induced by the family $\mathcal{F}$. We derive oracle inequalities and show that they are unimprovable in some sense. Numerical results demonstrate good practical behavior of the procedure.
dc.descriptionPublished in at http://dx.doi.org/10.1214/00-AOS576 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0704.2500
dc.identifierhttp://arxiv.org/abs/0704.2500
dc.identifierAnnals of Statistics 2009, Vol. 37, No. 1, 542-568
dc.identifierdoi:10.1214/00-AOS576
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222001
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62G05, 62G20 (Secondary)
dc.titleA universal procedure for aggregating estimators
dc.typetext

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