Block thresholding wavelet regression using SCAD penalty

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dc.contributor.authorPark, Cheolwooko
dc.date.accessioned2021-06-11T01:50:31Z-
dc.date.available2021-06-11T01:50:31Z-
dc.date.created2021-06-11-
dc.date.created2021-06-11-
dc.date.issued2010-09-
dc.identifier.citationJOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.140, no.9, pp.2755 - 2770-
dc.identifier.issn0378-3758-
dc.identifier.urihttp://hdl.handle.net/10203/285780-
dc.description.abstractThis paper concerns wavelet regression using a block thresholding procedure. Block thresholding methods utilize neighboring wavelet coefficients information to increase estimation accuracy. We propose to construct a data-driven block thresholding procedure using the smoothly clipped absolute deviation (SCAD) penalty. A simulation study demonstrates competitive finite sample performance of the proposed estimator compared to existing methods. We also show that the proposed estimator achieves optimal convergence rates in Besov spaces. (C) 2010 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.titleBlock thresholding wavelet regression using SCAD penalty-
dc.typeArticle-
dc.identifier.wosid000278398300037-
dc.identifier.scopusid2-s2.0-77952544005-
dc.type.rimsART-
dc.citation.volume140-
dc.citation.issue9-
dc.citation.beginningpage2755-
dc.citation.endingpage2770-
dc.citation.publicationnameJOURNAL OF STATISTICAL PLANNING AND INFERENCE-
dc.identifier.doi10.1016/j.jspi.2010.03.035-
dc.contributor.localauthorPark, Cheolwoo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorBesov space-
dc.subject.keywordAuthorBlock thresholding-
dc.subject.keywordAuthorConvergence rates-
dc.subject.keywordAuthorSmoothly clipped absolute deviation penalty-
dc.subject.keywordAuthorWavelet regression-
dc.subject.keywordPlusSHARP CHANGE-POINT-
dc.subject.keywordPlusVARIABLE SELECTION-
dc.subject.keywordPlusSHRINKAGE-
dc.subject.keywordPlusESTIMATORS-
dc.subject.keywordPlusDESIGN-
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