Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis
Systems and Control
2020-04-22 v1 Systems and Control
Statistics Theory
Statistics Theory
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.
Keywords
Cite
@article{arxiv.2004.09748,
title = {Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis},
author = {Timothy L. Molloy},
journal= {arXiv preprint arXiv:2004.09748},
year = {2020}
}
Comments
19 pages, 2 figures, Accepted for publication in IEEE Transactions on Automatic Control