On the performances of a new thresholding procedure using tree structure

dc.creatorAutin, Florent
dc.date2008-03-12
dc.date2008-05-23
dc.date.accessioned2026-07-07T12:17:35Z
dc.date.available2026-07-07T12:17:35Z
dc.descriptionThis paper deals with the problem of function estimation. Using the white noise model setting, we provide a method to construct a new wavelet procedure based on thresholding rules which takes advantage of the dyadic structure of the wavelet decomposition. We prove that this new procedure performs very well since, on the one hand, it is adaptive and near-minimax over a large class of Besov spaces and, on the other hand, the maximal functional space (maxiset) where this procedure attains a given rate of convergence is very large. More than this, by studying the shape of its maxiset, we prove that the new procedure outperforms the hard thresholding procedure.
dc.descriptionPublished in at http://dx.doi.org/10.1214/08-EJS205 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0803.1753
dc.identifierhttp://arxiv.org/abs/0803.1753
dc.identifierElectronic Journal of Statistics 2008, Vol. 2, 412-431
dc.identifierdoi:10.1214/08-EJS205
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212123
dc.subjectStatistics Theory
dc.subject62G05, 62G07 (Primary)
dc.titleOn the performances of a new thresholding procedure using tree structure
dc.typetext

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