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arXiv · 2310.09673

Robust Quickest Change Detection in Non-Stationary Processes

Yingze Hou·Yousef Oleyaeimotlagh·Rahul Mishra·Hoda Bidkhori·Taposh Banerjee
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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.

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Yingze Hou, Yousef Oleyaeimotlagh, Rahul Mishra, Hoda Bidkhori, Taposh Banerjee. 2023-10-14. Robust Quickest Change Detection in Non-Stationary Processes. https://arxiv.org/abs/2310.09673

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