Quickest Change Detection with Leave-one-out Density Estimation
Signal Processing
2022-11-07 v2 Statistics Theory
Statistics Theory
Abstract
The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is completely unknown. A window-limited leave-one-out (LOO) CuSum test is developed, which does not assume any knowledge of the post-change distribution, and does not require any post-change training samples. It is shown that, with certain convergence conditions on the density estimator, the LOO-CuSum test is first-order asymptotically optimal, as the false alarm rate goes to zero. The analysis is validated through numerical results, where the LOO-CuSum test is compared with baseline tests that have distributional knowledge.
Cite
@article{arxiv.2211.00223,
title = {Quickest Change Detection with Leave-one-out Density Estimation},
author = {Yuchen Liang and Venugopal V. Veeravalli},
journal= {arXiv preprint arXiv:2211.00223},
year = {2022}
}