Statistics and Its Interface

Volume 8 (2015)

Number 1

Special Issue on Extreme Theory and Application (Part II)

Guest Editors: Yazhen Wang and Zhengjun Zhang

Detecting change-points in extremes

Pages: 19 – 31



D. J. Dupuis (Department of Management Sciences, HEC Montréal, Québec, Canada)

Y. Sun (Department of Statistics, Ohio State University, Columbus, Oh., U.S.A.)

Huixia Judy Wang (Department of Statistics, George Washington University, Washington, D.C., U.S.A.)


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.


tail behavior, quantile methods

2010 Mathematics Subject Classification

Primary 62G32. Secondary 62P12.

Published 13 February 2015