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

5 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

@inproceedings{3d80170689ee4533a08986caac9b7c75,

title = "On parallel sequential change detection controlling false discovery rate",

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.",

author = "Jie Chen and Wenyi Zhang and Poor, {H. Vincent}",

note = "Funding Information: The work of J. Chen and W. Zhang was supported in part by the Foundation of China Scholarship Council and the National Natural Science Foundation of China under Grant 61379003, and the work of H. V. Poor was supported in part by the U.S. National Science Foundation under Grants CNS-1456793 and ECCS-1343210. Publisher Copyright: {\textcopyright} 2016 IEEE.; 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 ; Conference date: 06-11-2016 Through 09-11-2016",

year = "2017",

month = mar,

day = "1",

doi = "10.1109/ACSSC.2016.7869004",

language = "English (US)",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "107--111",

editor = "Matthews, {Michael B.}",

booktitle = "Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016",

address = "United States",

}

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

TY - GEN

T1 - On parallel sequential change detection controlling false discovery rate

AU - Chen, Jie

AU - Zhang, Wenyi

AU - Poor, H. Vincent

N1 - Funding Information: The work of J. Chen and W. Zhang was supported in part by the Foundation of China Scholarship Council and the National Natural Science Foundation of China under Grant 61379003, and the work of H. V. Poor was supported in part by the U.S. National Science Foundation under Grants CNS-1456793 and ECCS-1343210. Publisher Copyright: © 2016 IEEE.

PY - 2017/3/1

Y1 - 2017/3/1

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.

UR - http://www.scopus.com/inward/record.url?scp=85016311102&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016311102&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2016.7869004

DO - 10.1109/ACSSC.2016.7869004

M3 - Conference contribution

AN - SCOPUS:85016311102

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 107

EP - 111

BT - Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016

A2 - Matthews, Michael B.

PB - IEEE Computer Society

T2 - 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016

Y2 - 6 November 2016 through 9 November 2016

ER -

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

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this