Screening-Assisted Dynamic Multiple Testing with False Discovery Rate Control

In the era of big data, high-dimensional data always arrive in streams, making timely and accurate decision necessary. It has become particularly important to rapidly and sequentially identify individuals whose behavior deviates from the norm. Aiming at identifying as many irregular behavioral patterns as possible, the authors develop a large-scale dynamic testing system in the framework of false discovery rate (FDR) control. By fully exploiting the sequential feature of datastreams, the authors propose a screening-assisted procedure that filters streams and then only tests streams that pass the filter at each time point. A data-driven optimal screening threshold is derived, giving the new method an edge over existing methods. Under some mild conditions on the dependence structure of datastreams, the FDR is shown to be strongly controlled and the suggested approach for determining screening thresholds is asymptotically optimal. Simulation studies show that the proposed method is both accurate and powerful, and a real-data example is used for illustrative purpose.

Automated summary

In the era of big data, it is crucial to make timely and accurate decisions due to the arrival of high-dimensional data in streams. Identifying individuals with deviant behavior from the norm has become particularly important. The authors propose a large-scale dynamic testing system based on false discovery rate (FDR) control in order to detect as many irregular behavioral patterns as possible. By leveraging the sequential feature of datastreams, they develop a screening-assisted procedure that filters and tests streams in a sequential manner. The proposed method is shown to be accurate and powerful through simulation studies and a real-data example.