Detecting change-points in extremes

D. J. Dupuis, Ying Sun, Huixia Judy Wang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)19-31
Number of pages13
JournalStatistics and Its Interface
Issue number1
StatePublished - Feb 13 2015
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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