Robust Quickest Change Detection in Non-Stationary Processes
Methodology
2025-05-14 v3 Signal Processing
Statistics Theory
Statistics Theory
Abstract
Optimal algorithms are developed for robust detection of changes in non-stationary processes. These are processes in which the distribution of the data after change varies with time. The decision-maker does not have access to precise information on the post-change distribution. It is shown that if the post-change non-stationary family has a distribution that is least favorable in a well-defined sense, then the algorithms designed using the least favorable distributions are robust and optimal. Non-stationary processes are encountered in public health monitoring and space and military applications. The robust algorithms are applied to real and simulated data to show their effectiveness.
Cite
@article{arxiv.2310.09673,
title = {Robust Quickest Change Detection in Non-Stationary Processes},
author = {Yingze Hou and Yousef Oleyaeimotlagh and Rahul Mishra and Hoda Bidkhori and Taposh Banerjee},
journal= {arXiv preprint arXiv:2310.09673},
year = {2025}
}
Comments
A corrected version of the previous submission