Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection

• URL: http://arxiv.org/abs/2210.15060v1
• Date: Wed, 26 Oct 2022 22:05:29 GMT
• Title: Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection
• Authors: Song Wei, Chaofan Huang, Yao Xie
• Abstract summary: We present a novel scheme to boost detection power for kernel maximum mean discrepancy based sequential change-point detection procedures. Our proposed scheme features an optimal sub-sampling of the history data before the detection procedure, in order to tackle the power loss incurred by the random sub-sample from the enormous history data.
• Score: 10.969806056391004
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a novel scheme to boost detection power for kernel maximum mean discrepancy based sequential change-point detection procedures. Our proposed scheme features an optimal sub-sampling of the history data before the detection procedure, in order to tackle the power loss incurred by the random sub-sample from the enormous history data. We apply our proposed scheme to both Scan $B$ and Kernel Cumulative Sum (CUSUM) procedures, and improved performance is observed from extensive numerical experiments.

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