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June 2019 Sequential change-point detection based on nearest neighbors
Hao Chen
Ann. Statist. 47(3): 1381-1407 (June 2019). DOI: 10.1214/18-AOS1718


We propose a new framework for the detection of change-points in online, sequential data analysis. The approach utilizes nearest neighbor information and can be applied to sequences of multivariate observations or non-Euclidean data objects, such as network data. Different stopping rules are explored, and one specific rule is recommended due to its desirable properties. An accurate analytic approximation of the average run length is derived for the recommended rule, making it an easy off-the-shelf approach for real multivariate/object sequential data monitoring applications. Simulations reveal that the new approach has better performance than likelihood-based approaches for high dimensional data. The new approach is illustrated through a real dataset in detecting global structural changes in social networks.


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Hao Chen. "Sequential change-point detection based on nearest neighbors." Ann. Statist. 47 (3) 1381 - 1407, June 2019.


Received: 1 February 2017; Revised: 1 April 2018; Published: June 2019
First available in Project Euclid: 13 February 2019

zbMATH: 07053512
MathSciNet: MR3911116
Digital Object Identifier: 10.1214/18-AOS1718

Primary: 62G32
Secondary: 60K35

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

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.47 • No. 3 • June 2019
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