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Article: Extreme Values Identification in Regression Using a Peaks-Over-Threshold Approach

TitleExtreme Values Identification in Regression Using a Peaks-Over-Threshold Approach
Authors
Issue Date2015
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/02664763.asp
Citation
Journal of Applied Statistics, 2015, v. 42 n. 3, p. 566-576 How to Cite?
AbstractThe problem of heavy tail in regression models is studied. It is proposed that regression models are estimated by a standard procedure and a statistical check for heavy tail using residuals is conducted as a tool for regression diagnostic. Using the peaks-over-threshold approach, the generalized Pareto distribution quantifies the degree of heavy tail by the extreme value index. The number of excesses is determined by means of an innovative threshold model which partitions the random sample into extreme values and ordinary values. The overall decision on a significant heavy tail is justified by both a statistical test and a quantile–quantile plot. The usefulness of the approach includes justification of goodness of fit of the estimated regression model and quantification of the occurrence of extremal events. The proposed methodology is supplemented by surface ozone level in the city center of Leeds. © 2014, Taylor & Francis.
Persistent Identifierhttp://hdl.handle.net/10722/220214
ISSN
2015 Impact Factor: 0.419
2015 SCImago Journal Rankings: 0.501
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWong, ST-
dc.contributor.authorLi, WK-
dc.date.accessioned2015-10-16T06:32:49Z-
dc.date.available2015-10-16T06:32:49Z-
dc.date.issued2015-
dc.identifier.citationJournal of Applied Statistics, 2015, v. 42 n. 3, p. 566-576-
dc.identifier.issn0266-4763-
dc.identifier.urihttp://hdl.handle.net/10722/220214-
dc.description.abstractThe problem of heavy tail in regression models is studied. It is proposed that regression models are estimated by a standard procedure and a statistical check for heavy tail using residuals is conducted as a tool for regression diagnostic. Using the peaks-over-threshold approach, the generalized Pareto distribution quantifies the degree of heavy tail by the extreme value index. The number of excesses is determined by means of an innovative threshold model which partitions the random sample into extreme values and ordinary values. The overall decision on a significant heavy tail is justified by both a statistical test and a quantile–quantile plot. The usefulness of the approach includes justification of goodness of fit of the estimated regression model and quantification of the occurrence of extremal events. The proposed methodology is supplemented by surface ozone level in the city center of Leeds. © 2014, Taylor & Francis.-
dc.languageeng-
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/02664763.asp-
dc.relation.ispartofJournal of Applied Statistics-
dc.titleExtreme Values Identification in Regression Using a Peaks-Over-Threshold Approach-
dc.typeArticle-
dc.identifier.emailWong, ST: h0127272@hku.hk-
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hk-
dc.identifier.authorityLi, WK=rp00741-
dc.identifier.doi10.1080/02664763.2014.978843-
dc.identifier.scopuseid_2-s2.0-84918767000-
dc.identifier.hkuros255535-
dc.identifier.volume42-
dc.identifier.issue3-
dc.identifier.spage566-
dc.identifier.epage576-
dc.identifier.isiWOS:000346333600008-
dc.publisher.placeUnited Kingdom-

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