IMPROVED SIZER FOR TIME SERIES

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dc.contributor.authorPark, Cheolwooko
dc.contributor.authorHannig, Janko
dc.contributor.authorKang, Kee-Hoonko
dc.date.accessioned2021-06-11T01:30:47Z-
dc.date.available2021-06-11T01:30:47Z-
dc.date.created2021-06-11-
dc.date.created2021-06-11-
dc.date.issued2009-10-
dc.identifier.citationSTATISTICA SINICA, v.19, no.4, pp.1511 - 1530-
dc.identifier.issn1017-0405-
dc.identifier.urihttp://hdl.handle.net/10203/285763-
dc.description.abstractSiZer (SIgnificant ZERo crossing of the derivatives) is a scale-space visualization tool for statistical inferences. In this paper we improve global inference of SiZer for time series, originally proposed by Rondonotti, Marron and Park (2007), in two aspects. First, the estimation of the quantile in a confidence interval is theoretically justified by advanced distribution theory. Second, an improved non-parametric autocovariance function estimator is proposed using a differenced time series. A numerical study is conducted to demonstrate the sample performance of the proposed tool. In addition, asymptotic properties of SiZer for time series are investigated.-
dc.languageEnglish-
dc.publisherSTATISTICA SINICA-
dc.titleIMPROVED SIZER FOR TIME SERIES-
dc.typeArticle-
dc.identifier.wosid000271966500012-
dc.type.rimsART-
dc.citation.volume19-
dc.citation.issue4-
dc.citation.beginningpage1511-
dc.citation.endingpage1530-
dc.citation.publicationnameSTATISTICA SINICA-
dc.contributor.localauthorPark, Cheolwoo-
dc.contributor.nonIdAuthorHannig, Jan-
dc.contributor.nonIdAuthorKang, Kee-Hoon-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAutocovariance function estimation-
dc.subject.keywordAuthorlocal linear smoothing-
dc.subject.keywordAuthormultiple testing adjustment-
dc.subject.keywordAuthorSiZer-
dc.subject.keywordAuthorstatistical convergence-
dc.subject.keywordAuthortime series-
dc.subject.keywordPlusEXPLORATION-
dc.subject.keywordPlusFEATURES-
dc.subject.keywordPlusCURVES-
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