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arXiv · 2004.09748

Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis

Timothy L. Molloy
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Abstract

The problem of quickly diagnosing an unknown change in a stochastic process is studied. We establish novel bounds on the performance of misspecified diagnosis algorithms designed for changes that differ from those of the process, and pose and solve a new robust quickest change diagnosis problem in the asymptotic regime of few false alarms and false isolations. Simulations suggest that our asymptotically robust solution offers a computationally efficient alternative to generalised likelihood ratio algorithms.

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BibTeXRIS

Timothy L. Molloy. 2020-04-21. Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis. https://doi.org/10.1109/tac.2020.2985975

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