Efficient scalable schemes for monitoring a large number of data streams
成果类型：
Article
署名作者：
Mei, Y.
署名单位：
University System of Georgia; Georgia Institute of Technology
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asq010
发表日期：
2010
页码：
419433
关键词：
摘要：
The sequential changepoint detection problem is studied in the context of global online monitoring of a large number of independent data streams. We are interested in detecting an occurring event as soon as possible, but we do not know when the event will occur, nor do we know which subset of data streams will be affected by the event. A family of scalable schemes is proposed based on the sum of the local cumulative sum, CUSUM, statistics from each individual data stream, and is shown to asymptotically minimize the detection delays for each and every possible combination of affected data streams, subject to the global false alarm constraint. The usefulness and limitations of our asymptotic optimality results are illustrated by numerical simulations and heuristic arguments. The Appendices contain a probabilistic result on the first epoch to simultaneous record values for multiple independent random walks.