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Article: Diagnostic check for heavy tail in linear time series

TitleDiagnostic check for heavy tail in linear time series
Authors
KeywordsAutoregressive moving-average model
Change point
Extreme value index
Peaks-over-threshold
Residual
Issue Date1-Jan-2015
PublisherElsevier
Citation
Statistical Methodology, 2015, v. 24, p. 1-11 How to Cite?
Abstract

Justification of heavy tail is an important open problem. A systematic approach is proposed to verify heavy tail in linear time series. It consists of three parts, each of which is guided by statistical tests. The analysis is supplemented by an application to ozone concentration. The methodology has the advantage that the threshold selection is data-driven. Simulations show that test results are accurate even under model misspecification. The power is good under two heavy-tailed alternatives. The test is invariant when the time series clusters at extreme level in the study of the max-autoregressive process. It also gives a preliminary measure of tail heaviness if the underlying process is heavy-tailed.


Persistent Identifierhttp://hdl.handle.net/10722/369468
ISSN
2016 Impact Factor: 0.670
2019 SCImago Journal Rankings: 0.579

 

DC FieldValueLanguage
dc.contributor.authorWong, Siu Tung-
dc.date.accessioned2026-01-24T00:35:22Z-
dc.date.available2026-01-24T00:35:22Z-
dc.date.issued2015-01-01-
dc.identifier.citationStatistical Methodology, 2015, v. 24, p. 1-11-
dc.identifier.issn1572-3127-
dc.identifier.urihttp://hdl.handle.net/10722/369468-
dc.description.abstract<p>Justification of heavy tail is an important open problem. A systematic approach is proposed to verify heavy tail in linear time series. It consists of three parts, each of which is guided by statistical tests. The analysis is supplemented by an application to ozone concentration. The methodology has the advantage that the threshold selection is data-driven. Simulations show that test results are accurate even under model misspecification. The power is good under two heavy-tailed alternatives. The test is invariant when the time series clusters at extreme level in the study of the max-autoregressive process. It also gives a preliminary measure of tail heaviness if the underlying process is heavy-tailed.</p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofStatistical Methodology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectAutoregressive moving-average model-
dc.subjectChange point-
dc.subjectExtreme value index-
dc.subjectPeaks-over-threshold-
dc.subjectResidual-
dc.titleDiagnostic check for heavy tail in linear time series-
dc.typeArticle-
dc.identifier.doi10.1016/j.stamet.2014.11.001-
dc.identifier.scopuseid_2-s2.0-84953837691-
dc.identifier.volume24-
dc.identifier.spage1-
dc.identifier.epage11-
dc.identifier.issnl1572-3127-

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