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Activity Number: 622
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #318692 View Presentation
Title: Distribution-Free Detection of Structured Anomalies: Permutation and Rank-Based Scans
Author(s): Ery Arias-Castro and Rui Castro and Ervin Tanczos and Meng Wang*
Companies: University of California at San Diego and Technische Universiteit Eindhoven and Technische Universiteit Eindhoven and University of California at San Diego
Keywords: Distribution-Free ; Rank ; Scans

The scan statistic is by far the most popular method for anomaly detection, being popular in syndromic surveillance, signal and image processing, and target detection based on sensor networks, among other applications. The use of the scan statistics in such settings yields an hypothesis testing procedure, where the null hypothesis corresponds to the absence of anomalous behavior. If the null distribution is known, then calibration of a scan-based test is relatively easy, as it can be done by Monte-Carlo simulation. When the null distribution is unknown, it is less straightforward. We investigate two procedures. The first one is a calibration by permutation and the other is a rank-based scan test, which is distribution-free and less sensitive to outliers. Furthermore, the rank-scan test requires only a one-time calibration for a given data size making it computationally much more appealing. In both cases, we quantify the performance loss with respect to an oracle scan test that knows the null distribution. We show that using one of these calibration procedures results in only a very small loss of power in the context of a natural exponential family.

Authors who are presenting talks have a * after their name.

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