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.
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 -