The Annals of Statistics

Sequential change-point detection based on nearest neighbors

Hao Chen

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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.

Article information

Ann. Statist., Volume 47, Number 3 (2019), 1381-1407.

Received: February 2017
Revised: April 2018
First available in Project Euclid: 13 February 2019

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G32: Statistics of extreme values; tail inference
Secondary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]

Change-point sequential detection graph-based tests nonparametrics scan statistic tail probability high-dimensional data network data non-Euclidean data


Chen, Hao. Sequential change-point detection based on nearest neighbors. Ann. Statist. 47 (2019), no. 3, 1381--1407. doi:10.1214/18-AOS1718.

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Supplemental materials

  • Proofs for theorems. This supplement contains proofs for Theorem 4.2 and Theorem 4.4.