Quickest Change Detection with Confusing Change

• URL: http://arxiv.org/abs/2405.00842v1
• Date: Wed, 1 May 2024 20:10:06 GMT
• Title: Quickest Change Detection with Confusing Change
• Authors: Yu-Zhen Janice Chen, Jinhang Zuo, Venugopal V. Veeravalli, Don Towsley,
• Abstract summary: This work studies a QCD problem where the change is either a bad change, which we aim to detect, or a confusing change, which is not of our interest. We propose novel CuSum-based detection procedures, S-CuSum and J-CuSum, leveraging two CuSum statistics. For both S-CuSum and J-CuSum, we provide analytical performance guarantees and validate them by numerical results.
• Score: 26.769246781414545
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the problem of quickest change detection (QCD), a change occurs at some unknown time in the distribution of a sequence of independent observations. This work studies a QCD problem where the change is either a bad change, which we aim to detect, or a confusing change, which is not of our interest. Our objective is to detect a bad change as quickly as possible while avoiding raising a false alarm for pre-change or a confusing change. We identify a specific set of pre-change, bad change, and confusing change distributions that pose challenges beyond the capabilities of standard Cumulative Sum (CuSum) procedures. Proposing novel CuSum-based detection procedures, S-CuSum and J-CuSum, leveraging two CuSum statistics, we offer solutions applicable across all kinds of pre-change, bad change, and confusing change distributions. For both S-CuSum and J-CuSum, we provide analytical performance guarantees and validate them by numerical results. Furthermore, both procedures are computationally efficient as they only require simple recursive updates.

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