On parallel sequential change detection controlling false discovery rate

Jie Chen; Wenyi Zhang; H. Vincent Poor

doi:10.1109/ACSSC.2016.7869004

On parallel sequential change detection controlling false discovery rate

Jie Chen, Wenyi Zhang, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

In some recent applications involving large-scale data analytics, a plurality of data streams are sequentially observed in parallel, and the statistical decision maker is asked to screen out among these data streams those that exhibit certain characteristics. Motivated by such setting, in this work, a parallel sequential change detection model is investigated. In the model, a plurality of independent parallel data streams, each of which has a change-point with a certain prior probability distribution, are sequentially observed with a maximum sampling constraint. A sequential procedure is developed to inspect these parallel data streams and to decide, for each of them, whether a change has occurred. The sequential procedure is shown to guarantee the false discovery rate (FDR). The average detection delay over the parallel data streams is also quantified in asymptotic regimes. Numerical experiments are conducted to illustrate the proposed sequential procedure.

Original language	English (US)
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	107-111
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869004
State	Published - Mar 1 2017
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2016.7869004

Cite this

Chen, J., Zhang, W., & Poor, H. V. (2017). On parallel sequential change detection controlling false discovery rate. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 107-111). Article 7869004 (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869004

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abstract = "In some recent applications involving large-scale data analytics, a plurality of data streams are sequentially observed in parallel, and the statistical decision maker is asked to screen out among these data streams those that exhibit certain characteristics. Motivated by such setting, in this work, a parallel sequential change detection model is investigated. In the model, a plurality of independent parallel data streams, each of which has a change-point with a certain prior probability distribution, are sequentially observed with a maximum sampling constraint. A sequential procedure is developed to inspect these parallel data streams and to decide, for each of them, whether a change has occurred. The sequential procedure is shown to guarantee the false discovery rate (FDR). The average detection delay over the parallel data streams is also quantified in asymptotic regimes. Numerical experiments are conducted to illustrate the proposed sequential procedure.",

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Chen, J, Zhang, W & Poor, HV 2017, On parallel sequential change detection controlling false discovery rate. in MB Matthews (ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869004, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 107-111, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869004

On parallel sequential change detection controlling false discovery rate. / Chen, Jie; Zhang, Wenyi; Poor, H. Vincent.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. ed. / Michael B. Matthews. IEEE Computer Society, 2017. p. 107-111 7869004 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - In some recent applications involving large-scale data analytics, a plurality of data streams are sequentially observed in parallel, and the statistical decision maker is asked to screen out among these data streams those that exhibit certain characteristics. Motivated by such setting, in this work, a parallel sequential change detection model is investigated. In the model, a plurality of independent parallel data streams, each of which has a change-point with a certain prior probability distribution, are sequentially observed with a maximum sampling constraint. A sequential procedure is developed to inspect these parallel data streams and to decide, for each of them, whether a change has occurred. The sequential procedure is shown to guarantee the false discovery rate (FDR). The average detection delay over the parallel data streams is also quantified in asymptotic regimes. Numerical experiments are conducted to illustrate the proposed sequential procedure.

AB - In some recent applications involving large-scale data analytics, a plurality of data streams are sequentially observed in parallel, and the statistical decision maker is asked to screen out among these data streams those that exhibit certain characteristics. Motivated by such setting, in this work, a parallel sequential change detection model is investigated. In the model, a plurality of independent parallel data streams, each of which has a change-point with a certain prior probability distribution, are sequentially observed with a maximum sampling constraint. A sequential procedure is developed to inspect these parallel data streams and to decide, for each of them, whether a change has occurred. The sequential procedure is shown to guarantee the false discovery rate (FDR). The average detection delay over the parallel data streams is also quantified in asymptotic regimes. Numerical experiments are conducted to illustrate the proposed sequential procedure.

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Chen J, Zhang W, Poor HV. On parallel sequential change detection controlling false discovery rate. In Matthews MB, editor, Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. IEEE Computer Society. 2017. p. 107-111. 7869004. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2016.7869004

On parallel sequential change detection controlling false discovery rate

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