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Multiple Change-Point Detection: A Selective Overview

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.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/622820
Date11 1900
CreatorsNiu, Yue S., Hao, Ning, Zhang, Heping
ContributorsUniv Arizona, Dept Math
PublisherINST MATHEMATICAL STATISTICS
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
TypeArticle
Rights© Institute of Mathematical Statistics, 2016
Relationhttp://projecteuclid.org/euclid.ss/1484816589

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