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February 2015 Graph-based change-point detection
Hao Chen, Nancy Zhang
Ann. Statist. 43(1): 139-176 (February 2015). DOI: 10.1214/14-AOS1269


We consider the testing and estimation of change-points—locations where the distribution abruptly changes—in a data sequence. A new approach, based on scan statistics utilizing graphs representing the similarity between observations, is proposed. The graph-based approach is nonparametric, and can be applied to any data set as long as an informative similarity measure on the sample space can be defined. Accurate analytic approximations to the significance of graph-based scan statistics for both the single change-point and the changed interval alternatives are provided. Simulations reveal that the new approach has better power than existing approaches when the dimension of the data is moderate to high. The new approach is illustrated on two applications: The determination of authorship of a classic novel, and the detection of change in a network over time.


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Hao Chen. Nancy Zhang. "Graph-based change-point detection." Ann. Statist. 43 (1) 139 - 176, February 2015.


Published: February 2015
First available in Project Euclid: 18 November 2014

zbMATH: 1308.62090
MathSciNet: MR3285603
Digital Object Identifier: 10.1214/14-AOS1269

Primary: 62G32

Keywords: Change-point , complex data , graph-based tests , High-dimensional data , network data , non-Euclidean data , nonparametrics , scan statistic , tail probability

Rights: Copyright © 2015 Institute of Mathematical Statistics


Vol.43 • No. 1 • February 2015
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