The Annals of Statistics
- Ann. Statist.
- Volume 47, Number 3 (2019), 1381-1407.
Sequential change-point detection based on nearest neighbors
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.
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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Chen, Hao. Sequential change-point detection based on nearest neighbors. Ann. Statist. 47 (2019), no. 3, 1381--1407. doi:10.1214/18-AOS1718. https://projecteuclid.org/euclid.aos/1550026842
- Proofs for theorems. This supplement contains proofs for Theorem 4.2 and Theorem 4.4.