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Compound sequential change-point detection in parallel data streams

Chen, Yunxiao and Li, Xiaoou (2023) Compound sequential change-point detection in parallel data streams. Statistica Sinica. ISSN 1017-0405 (In Press)

[img] Text (Compound sequential change-point detection in parallel data streams) - Accepted Version
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Identification Number: 10.5705/ss.202020.0508

Abstract

We consider sequential change-point detection in parallel data streams, where each stream has its own change point. Once a change is detected in a data stream, this stream is deactivated permanently. The goal is to maximize the normal operation of the pre-change streams, while controlling the proportion of post-change streams among the active streams at all time points. Taking a Bayesian formulation, we develop a compound decision framework for this problem. A procedure is proposed that is uniformly optimal among all sequential procedures which control the expected proportion of post-change streams at all time points. We also investigate the asymptotic behavior of the proposed method when the number of data streams grows large. Numerical examples are provided to illustrate the use and performance of the proposed method.

Item Type: Article
Official URL: http://www3.stat.sinica.edu.tw/statistica/
Additional Information: © 2021 Institute of Statistical Science
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 09 Jul 2021 09:42
Last Modified: 10 Aug 2021 13:30
URI: http://eprints.lse.ac.uk/id/eprint/111010

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