Back To Index Previous Article Next Article Full Text

Statistica Sinica 29 (2019), 1-22

SCALABLE SUM-SHRINKAGE SCHEMES FOR
DISTRIBUTED MONITORING LARGE-SCALE
DATA STREAMS
Kun Liu, Ruizhi Zhang and Yajun Mei
Georgia Institute of Technology

Abstract: In this article, we investigate the problem of monitoring independent large-scale data streams where an undesired event may occur at some unknown time and affect only a few unknown data streams. Motivated by parallel and distributed computing, we propose to develop scalable global monitoring schemes by parallel running local detection procedures and by using the sum of the shrinkage transformation of local detection statistics as a global statistic to make a decision. The usefulness of our proposed SUM-Shrinkage approach is illustrated in an example of monitoring large-scale independent normally distributed data streams when the local post-change mean shifts are unknown and can be positive or negative.

Key words and phrases: Change-point, CUSUM, parallel computing, quickest detection, sensor networks.

Back To Index Previous Article Next Article Full Text