False Discovery Rate Approach to Dynamic Change Detection
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 105224 |
Number of pages | 19 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 198 |
Online published | 1 Aug 2023 |
Publication status | Published - Nov 2023 |
Link(s)
Abstract
In multiple data stream surveillance, the rapid and sequential identification of individuals whose behaviour deviates from the norm has become particularly important. In such applications, the state of a stream can alternate, possibly multiple times, between a null state and an alternative state. To balance the ability to detect two types of changes, that is, a change from the null to the alternative and back to the null, we propose a new multiple testing procedure based on a penalized version of the generalized likelihood ratio test statistics for change detection. The false discovery rate (FDR) at each time point is shown to be controlled under some mild conditions on the dependence structure of data streams. A data-driven approach is developed for selection of the penalization parameter. Its advantage is demonstrated via simulation and a data example. © 2023 Elsevier Inc.
Research Area(s)
- High-dimensional data streams, Multiple epidemic changes, Multiple testing, Penalized methods, Sequential detection
Citation Format(s)
False Discovery Rate Approach to Dynamic Change Detection. / Du, Lilun; Wen, Mengtao.
In: Journal of Multivariate Analysis, Vol. 198, 105224, 11.2023.
In: Journal of Multivariate Analysis, Vol. 198, 105224, 11.2023.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review