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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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.
AbstractList 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.
Author Moushegian, Sean
Ding, Jie
Banerjee, Taposh
Wu, Suya
Tarokh, Vahid
Diao, Enmao
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SubjectTerms 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
Title Robust Score-Based Quickest Change Detection
URI https://ieeexplore.ieee.org/document/10982143
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