@inproceedings{43a83c75a87b47b5b229ae9ea16f12eb,
title = "Quickest change detection with unknown post-change distribution",
abstract = "This paper considers the problem of quickest detection of a change in distribution under the assumption that the pre-change distribution π is known, and the post-change distribution μ is unknown and belongs to a general class of distributions. Using the knowledge of the pre-change distribution π, the sample space is partitioned into equiprobable intervals and the number of samples falling into each of these intervals is monitored to detect the change. A test statistic that approximates the generalized likelihood ratio test is proposed. A recursive update scheme to compute the statistic efficiently and an approximation of the average run-length to false alarm are also derived. Simulations show that our approach is comparable in performance to two other non-parametric quickest change detection methods if the change is either a shift in distribution mean or variance, respectively. But our method significantly outperforms them if these distribution change assumptions are violated.",
keywords = "ARL approximation, GLRT, Quickest change detection, non-parametric, unknown post-change distribution",
author = "Lau, {Tze Siong} and Tay, {Wee Peng} and Veeravalli, {Venugopal V.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952892",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3924--3928",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}