Robust Score-Based Quickest Change Detection
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866908438587506688 |
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| author | Moushegian, Sean Wu, Suya Diao, Enmao Ding, Jie Banerjee, Taposh Tarokh, Vahid |
| author_facet | Moushegian, Sean Wu, Suya Diao, Enmao Ding, Jie Banerjee, Taposh Tarokh, Vahid |
| contents | Methods in the field of quickest change detection rapidly detect in real-time a change in the data-generating distribution of an online data stream. Existing methods have been able to detect this change point when the densities of the pre- and post-change distributions are known. Recent work has extended these results to the case where the pre- and post-change distributions are known only by their score functions. This work considers the case where the pre- and post-change score functions are known only to correspond to distributions in two disjoint sets. This work selects a pair of least-favorable distributions from these sets to robustify the existing score-based quickest change detection algorithm, the properties of which are studied. This paper calculates the least-favorable distributions for specific model classes and provides methods of estimating the least-favorable distributions for common constructions. Simulation results are provided demonstrating the performance of our robust change detection algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11094 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Score-Based Quickest Change Detection Moushegian, Sean Wu, Suya Diao, Enmao Ding, Jie Banerjee, Taposh Tarokh, Vahid Methodology Signal Processing Machine Learning Methods in the field of quickest change detection rapidly detect in real-time a change in the data-generating distribution of an online data stream. Existing methods have been able to detect this change point when the densities of the pre- and post-change distributions are known. Recent work has extended these results to the case where the pre- and post-change distributions are known only by their score functions. This work considers the case where the pre- and post-change score functions are known only to correspond to distributions in two disjoint sets. This work selects a pair of least-favorable distributions from these sets to robustify the existing score-based quickest change detection algorithm, the properties of which are studied. This paper calculates the least-favorable distributions for specific model classes and provides methods of estimating the least-favorable distributions for common constructions. Simulation results are provided demonstrating the performance of our robust change detection algorithm. |
| title | Robust Score-Based Quickest Change Detection |
| topic | Methodology Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2407.11094 |