Quickest Change Detection with Confusing Change
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913339107442688 |
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| author | Chen, Yu-Zhen Janice Zuo, Jinhang Veeravalli, Venugopal V. Towsley, Don |
| author_facet | Chen, Yu-Zhen Janice Zuo, Jinhang Veeravalli, Venugopal V. Towsley, Don |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00842 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quickest Change Detection with Confusing Change Chen, Yu-Zhen Janice Zuo, Jinhang Veeravalli, Venugopal V. Towsley, Don Statistics Theory Information Theory Machine Learning Signal Processing Optimization and Control 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. |
| title | Quickest Change Detection with Confusing Change |
| topic | Statistics Theory Information Theory Machine Learning Signal Processing Optimization and Control |
| url | https://arxiv.org/abs/2405.00842 |