Robust Score-Based Quickest Change Detection

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 ext...

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Vydané v:IEEE transactions on information theory Ročník 71; číslo 7; s. 5539 - 5555
Hlavní autori: Moushegian, Sean, Wu, Suya, Diao, Enmao, Ding, Jie, Banerjee, Taposh, Tarokh, Vahid
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2025
Predmet:
Change detection algorithms
change-point detection
Computational modeling
Delays
Detection algorithms
Heart rate
Quickest change detection
Reviews
robust detection
score-based methods
Simulation
Stochastic processes
Training
Uncertainty
ISSN:0018-9448, 1557-9654
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Popis
Shrnutí: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.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2025.3566677