http://www.cnr.it/ontology/cnr/individuo/prodotto/ID7713
Convergence of the Red-TOWER Method for removing noise from data (Articolo in rivista)
- Type
- Label
- Convergence of the Red-TOWER Method for removing noise from data (Articolo in rivista) (literal)
- Anno
- 2001-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Amato U.(*), Jin Q.(**) (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- (*)Istituto per le APplicazioni del Calcolo 'Mauro Picone' CNR; (**)Department of Mathematics, Purdue University, West Lafayette (USA) (literal)
- Titolo
- Convergence of the Red-TOWER Method for removing noise from data (literal)
- Abstract
- By coupling the wavelet transform with a particular
nonlinear shrinking function, the Red-telescopic optimal
wavelet estimation of the risk (TOWER) method is introduced for
removing noise from signals. It is shown that the method yields
convergence of the L2 risk to the actual solution with optimal rate.
Moreover, the method is proved to be asymptotically efficient when
the regularization parameter is selected by the generalized cross
validation criterion (GCV) or the Mallows criterion. Numerical
experiments based on synthetic data are provided to compare the
performance of the Red-TOWER method with hard-thresholding,
soft-thresholding, and neighcoeff thresholding. Furthermore, the
numerical tests are also performed when the TOWER method is
applied to hard-thresholding, soft-thresholding, and neighcoeff
thresholding, for which the full convergence results are still open. (literal)
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