Open Access
April 2013 Sequential multi-sensor change-point detection
Yao Xie, David Siegmund
Ann. Statist. 41(2): 670-692 (April 2013). DOI: 10.1214/13-AOS1094


We develop a mixture procedure to monitor parallel streams of data for a change-point that affects only a subset of them, without assuming a spatial structure relating the data streams to one another. Observations are assumed initially to be independent standard normal random variables. After a change-point the observations in a subset of the streams of data have nonzero mean values. The subset and the post-change means are unknown. The procedure we study uses stream specific generalized likelihood ratio statistics, which are combined to form an overall detection statistic in a mixture model that hypothesizes an assumed fraction $p_{0}$ of affected data streams. An analytic expression is obtained for the average run length (ARL) when there is no change and is shown by simulations to be very accurate. Similarly, an approximation for the expected detection delay (EDD) after a change-point is also obtained. Numerical examples are given to compare the suggested procedure to other procedures for unstructured problems and in one case where the problem is assumed to have a well-defined geometric structure. Finally we discuss sensitivity of the procedure to the assumed value of $p_{0}$ and suggest a generalization.


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Yao Xie. David Siegmund. "Sequential multi-sensor change-point detection." Ann. Statist. 41 (2) 670 - 692, April 2013.


Published: April 2013
First available in Project Euclid: 26 April 2013

zbMATH: 1267.62084
MathSciNet: MR3023983
Digital Object Identifier: 10.1214/13-AOS1094

Primary: 62L10

Keywords: change-point detection , multi-sensor

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.41 • No. 2 • April 2013
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