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    Multiple Change-Point Detection: A Selective Overview

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    euclid.ss.1484816589.pdf
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    Author
    Niu, Yue S.
    Hao, Ning
    Zhang, Heping
    Affiliation
    Univ Arizona, Dept Math
    Issue Date
    2016-11
    Keywords
    Binary segmentation
    consistency
    multiple testing
    normal mean change-point model
    regression
    screening and ranking algorithm
    
    Metadata
    Show full item record
    Publisher
    INST MATHEMATICAL STATISTICS
    Citation
    Multiple Change-Point Detection: A Selective Overview 2016, 31 (4):611 Statistical Science
    Journal
    Statistical Science
    Rights
    © Institute of Mathematical Statistics, 2016.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    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.
    ISSN
    0883-4237
    DOI
    10.1214/16-STS587
    Version
    Final published version
    Sponsors
    National Science Foundation [DMS-13-09507]; National Institutes of Health [R01 DA016750]
    Additional Links
    http://projecteuclid.org/euclid.ss/1484816589
    ae974a485f413a2113503eed53cd6c53
    10.1214/16-STS587
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