Detecting change-points in extremes

Handle URI:
http://hdl.handle.net/10754/550889
Title:
Detecting change-points in extremes
Authors:
Dupuis, D. J.; Sun, Ying ( 0000-0001-6703-4270 ) ; Wang, Huixia Judy
Abstract:
Even though most work on change-point estimation focuses on changes in the mean, changes in the variance or in the tail distribution can lead to more extreme events. In this paper, we develop a new method of detecting and estimating the change-points in the tail of multiple time series data. In addition, we adapt existing tail change-point detection methods to our specific problem and conduct a thorough comparison of different methods in terms of performance on the estimation of change-points and computational time. We also examine three locations on the U.S. northeast coast and demonstrate that the methods are useful for identifying changes in seasonally extreme warm temperatures.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Detecting change-points in extremes 2015, 8 (1):19 Statistics and Its Interface
Journal:
Statistics and Its Interface
Issue Date:
1-Jan-2015
DOI:
10.4310/SII.2015.v8.n1.a3
Type:
Article
ISSN:
19387989; 19387997
Additional Links:
http://www.intlpress.com/site/pub/pages/journals/items/sii/content/vols/0008/0001/a003/
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDupuis, D. J.en
dc.contributor.authorSun, Yingen
dc.contributor.authorWang, Huixia Judyen
dc.date.accessioned2015-04-29T13:25:42Zen
dc.date.available2015-04-29T13:25:42Zen
dc.date.issued2015-01-01en
dc.identifier.citationDetecting change-points in extremes 2015, 8 (1):19 Statistics and Its Interfaceen
dc.identifier.issn19387989en
dc.identifier.issn19387997en
dc.identifier.doi10.4310/SII.2015.v8.n1.a3en
dc.identifier.urihttp://hdl.handle.net/10754/550889en
dc.description.abstractEven though most work on change-point estimation focuses on changes in the mean, changes in the variance or in the tail distribution can lead to more extreme events. In this paper, we develop a new method of detecting and estimating the change-points in the tail of multiple time series data. In addition, we adapt existing tail change-point detection methods to our specific problem and conduct a thorough comparison of different methods in terms of performance on the estimation of change-points and computational time. We also examine three locations on the U.S. northeast coast and demonstrate that the methods are useful for identifying changes in seasonally extreme warm temperatures.en
dc.relation.urlhttp://www.intlpress.com/site/pub/pages/journals/items/sii/content/vols/0008/0001/a003/en
dc.rightsArchived with thanks to Statistics and Its Interfaceen
dc.subjecttail behavioren
dc.subjectquantile methodsen
dc.titleDetecting change-points in extremesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalStatistics and Its Interfaceen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Management Sciences, HEC Montréal, Québec, Canadaen
dc.contributor.institutionDepartment of Statistics, Ohio State University, Columbus, Oh., U.S.A.en
dc.contributor.institutionDepartment of Statistics, George Washington University, Washington, D.C., U.S.A.en
kaust.authorSun, Yingen
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