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Titolo:
Wavelet shrinkage for correlated data and inverse problems: Adaptivity results
Autore:
Johnstone, IM;
Indirizzi:
Stanford Univ, Dept Stat, Stanford, CA 94305 USA Stanford Univ Stanford CA USA 94305 iv, Dept Stat, Stanford, CA 94305 USA
Titolo Testata:
STATISTICA SINICA
fascicolo: 1, volume: 9, anno: 1999,
pagine: 51 - 83
SICI:
1017-0405(199901)9:1<51:WSFCDA>2.0.ZU;2-2
Fonte:
ISI
Lingua:
ENG
Soggetto:
NOISE;
Keywords:
adaptation; correlated data; fractional Brownian motion; linear inverse problems; long range dependence; mixing conditions; oracle inequalities; rates of convergence; unbiased risk estimate; wavelet Vaguelette decomposition; wavelet shrinkage; wavelet thresholding;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
20
Recensione:
Indirizzi per estratti:
Indirizzo: Johnstone, IM Stanford Univ, Dept Stat, Stanford, CA 94305 USA Stanford Univ Stanford CA USA 94305 Stanford, CA 94305 USA
Citazione:
I.M. Johnstone, "Wavelet shrinkage for correlated data and inverse problems: Adaptivity results", STAT SINICA, 9(1), 1999, pp. 51-83

Abstract

Johnstone and Silverman (1997) described a level-dependent thresholding method for extracting signals from correlated noise. The thresholds were chosen to minimize a data based unbiased risk criterion. Here we show that in certain asymptotic models encompassing short and long range dependence, these methods are simultaneously asymptotically minimax up to constants over a broad range of Besov classes. We indicate the extension of the methods and results to a class of linear inverse problems possessing a wavelet vaguelette decomposition.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 14/07/20 alle ore 13:20:05