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arXiv · 2407.11094

Robust Score-Based Quickest Change Detection

Sean Moushegian·Suya Wu·Enmao Diao·Jie Ding·Taposh Banerjee·Vahid Tarokh
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Abstract

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.

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Sean Moushegian, Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh. 2024-07-15. Robust Score-Based Quickest Change Detection. https://doi.org/10.1109/tit.2025.3566677

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