Robust Quickest Change Detection with Cost-Efficient Experiment Design and Unknown Post-Change Model
Abstract
A quickest change detection problem is studied where the decision-maker has access to multiple experiments, but can choose only one experiment at a time to detect the change. The true post-change distribution under each experiment is unknown, but known to belong to a family of distributions. It is assumed that each of these families of distributions has an element that is least favorable in a well-defined sense. An algorithm is proposed that is designed using the least favorable distributions. It is shown that the algorithm is first-order asymptotically optimal, as the rate of false alarms goes to zero. This algorithm minimizes the worst-case average detection delay over all post-change distributions within the assumed families, subject to a constraint on the average run length to false alarm and constraints on the average fraction of time each experiment is performed before change.
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BibTeX
@misc{lubenia2026robust,
title = {{Robust Quickest Change Detection with Cost-Efficient Experiment Design and Unknown Post-Change Model}},
author = {Patrick Vincent Lubenia and Taposh Banerjee},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16055},
eprint = {2610.16055},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16055},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Robust Quickest Change Detection with Cost-Efficient Experiment Design and Unknown Post-Change Model
AU - Patrick Vincent Lubenia
AU - Taposh Banerjee
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.16055
UR - https://arxiv.org/abs/2610.16055
AB - A quickest change detection problem is studied where the decision-maker has access to multiple experiments, but can choose only one experiment at a time to detect the change. The true post-change distribution under each experiment is unknown, but known to belong to a family of distributions. It is assumed that each of these families of distributions has an element that is least favorable in a well-defined sense. An algorithm is proposed that is designed using the least favorable distributions. It is shown that the algorithm is first-order asymptotically optimal, as the rate of false alarms goes to zero. This algorithm minimizes the worst-case average detection delay over all post-change distributions within the assumed families, subject to a constraint on the average run length to false alarm and constraints on the average fraction of time each experiment is performed before change.
ER -