TITLE: Monitoring a Large Number of Data Streams via Thresholding

SPEAKER: Yajun Mei

ABSTRACT:

In the modern information age one often monitors a large number of data streams with the aim of offering the potential for early detection of a "trigger" event. In this talk, we are interested in detecting the 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. Motivated by the applications in censoring sensor networks and by the case when one has a prior knowledge that at most r data streams will be affected, we propose scalable global monitoring schemes based on the sum of the local detection statistics that are "large" under either hard thresholding or top-r thresholding rules or both. The proposed schemes are shown to possess certain asymptotic optimality properties.