Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146209
Title: Quickest change detection in the presence of a nuisance change
Authors: Lau, Tze Siong
Tay, Wee Peng
Keywords: Science::Mathematics::Statistics
Issue Date: 2019
Source: Lau, T. S., & Tay, W. P. (2019). Quickest change detection in the presence of a nuisance change. IEEE Transactions on Signal Processing, 67(20), 5281-5296. doi:10.1109/TSP.2019.2939080
Journal: IEEE Transactions on Signal Processing
Abstract: In the quickest change detection problem in which both nuisance and critical changes may occur, the objective is to detect the critical change as quickly as possible without raising an alarm when either there is no change or a nuisance change has occurred. A window-limited sequential change detection procedure based on the generalized likelihood ratio test statistic is proposed. A recursive update scheme for the proposed test statistic is developed and is shown to be asymptotically optimal under mild technical conditions. In the scenario where the post-change distribution belongs to a parametrized family, a generalized stopping time and a lower bound on its average run length are derived. The proposed stopping rule is compared with the finite moving average (FMA) stopping time and the naive 2-stage procedure that detects the nuisance or critical change using separate CuSum stopping procedures for the nuisance and critical changes. Simulations demonstrate that the proposed rule outperforms the FMA stopping time and the 2-stage procedure, and experiments on a real dataset on bearing failure verify the performance of the proposed stopping time.
URI: https://hdl.handle.net/10356/146209
ISSN: 1053-587X
DOI: 10.1109/TSP.2019.2939080
Schools: School of Electrical and Electronic Engineering 
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSP.2019.2939080
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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