Robust Score-Based Quickest Change Detection
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.
Cite
@article{arxiv.2407.11094,
title = {Robust Score-Based Quickest Change Detection},
author = {Sean Moushegian and Suya Wu and Enmao Diao and Jie Ding and Taposh Banerjee and Vahid Tarokh},
journal= {arXiv preprint arXiv:2407.11094},
year = {2025}
}
Comments
Accepted manuscript. Published in IEEE Transactions on Information Theory. arXiv admin note: text overlap with arXiv:2306.05091