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- Publisher Website: 10.1080/02664763.2014.978843
- Scopus: eid_2-s2.0-84918767000
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Article: Extreme Values Identification in Regression Using a Peaks-Over-Threshold Approach
Title | Extreme Values Identification in Regression Using a Peaks-Over-Threshold Approach |
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Authors | |
Keywords | exponential threshold model extreme value index ozone peaks-over-threshold regression diagnostic |
Issue Date | 2015 |
Publisher | Routledge. 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? |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/220214 |
ISSN | 2023 Impact Factor: 1.2 2023 SCImago Journal Rankings: 0.545 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wong, ST | - |
dc.contributor.author | Li, WK | - |
dc.date.accessioned | 2015-10-16T06:32:49Z | - |
dc.date.available | 2015-10-16T06:32:49Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Journal of Applied Statistics, 2015, v. 42 n. 3, p. 566-576 | - |
dc.identifier.issn | 0266-4763 | - |
dc.identifier.uri | http://hdl.handle.net/10722/220214 | - |
dc.description.abstract | The 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.language | eng | - |
dc.publisher | Routledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/02664763.asp | - |
dc.relation.ispartof | Journal of Applied Statistics | - |
dc.subject | exponential threshold model | - |
dc.subject | extreme value index | - |
dc.subject | ozone | - |
dc.subject | peaks-over-threshold | - |
dc.subject | regression diagnostic | - |
dc.title | Extreme Values Identification in Regression Using a Peaks-Over-Threshold Approach | - |
dc.type | Article | - |
dc.identifier.email | Wong, ST: h0127272@hku.hk | - |
dc.identifier.email | Li, WK: hrntlwk@hkucc.hku.hk | - |
dc.identifier.authority | Li, WK=rp00741 | - |
dc.identifier.doi | 10.1080/02664763.2014.978843 | - |
dc.identifier.scopus | eid_2-s2.0-84918767000 | - |
dc.identifier.hkuros | 255535 | - |
dc.identifier.volume | 42 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 566 | - |
dc.identifier.epage | 576 | - |
dc.identifier.isi | WOS:000346333600008 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0266-4763 | - |