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Online kernel CUSUM for change-point detection

Song Wei & Yao Xie

Journal of the Royal Statistical Society. Series B: Statistical Methodology2026https://doi.org/10.1093/jrsssb/qkag020article
AJG 4
Weight
0.50

Abstract

We present a computationally efficient online kernel Cumulative Sum method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location. Our approach exhibits increased sensitivity to small changes compared to existing kernel-based change-point detection methods, including the Scan-B statistic, corresponding to a non-parametric Shewhart chart-type procedure. We provide accurate analytic approximations for two key performance metrics: the average run length (ARL) and expected detection delay, which enable us to establish an optimal window length to be on the order of the logarithm of ARL to ensure minimal power loss relative to an oracle procedure with infinite memory. Moreover, we introduce a recursive calculation procedure for detection statistics to ensure constant computational and memory complexity, which is essential for online implementation. Through extensive experiments on both simulated and real data, we demonstrate the competitive performance of our method and validate our theoretical results.

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https://doi.org/https://doi.org/10.1093/jrsssb/qkag020

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@article{song2026,
  title        = {{Online kernel CUSUM for change-point detection}},
  author       = {Song Wei & Yao Xie},
  journal      = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/jrsssb/qkag020},
}

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0.50

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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