AffiliationUniv Arizona, Dept Math
normal mean change-point model
screening and ranking algorithm
MetadataShow full item record
PublisherINST MATHEMATICAL STATISTICS
CitationMultiple Change-Point Detection: A Selective Overview 2016, 31 (4):611 Statistical Science
Rights© Institute of Mathematical Statistics, 2016.
Collection InformationThis 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 firstname.lastname@example.org.
AbstractVery 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.
VersionFinal published version
SponsorsNational Science Foundation [DMS-13-09507]; National Institutes of Health [R01 DA016750]