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November 2016 Multiple Change-Point Detection: A Selective Overview
Yue S. Niu, Ning Hao, Heping Zhang
Statist. Sci. 31(4): 611-623 (November 2016). DOI: 10.1214/16-STS587


Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.


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Yue S. Niu. Ning Hao. Heping Zhang. "Multiple Change-Point Detection: A Selective Overview." Statist. Sci. 31 (4) 611 - 623, November 2016.


Published: November 2016
First available in Project Euclid: 19 January 2017

zbMATH: 06946254
MathSciNet: MR3598742
Digital Object Identifier: 10.1214/16-STS587

Keywords: binary segmentation , consistency , multiple testing , normal mean change-point model , regression , screening and ranking algorithm

Rights: Copyright © 2016 Institute of Mathematical Statistics


Vol.31 • No. 4 • November 2016
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