Anomaly Detection for a Large Number of Streams: A Permutation-Based Higher Criticism Approach

Ivo V. Stoepker, Rui M. Castro, Ery Arias-Castro & Edwin van den Heuvel
Anomaly detection when observing a large number of data streams is essential in a variety of applications, ranging from epidemiological studies to monitoring of complex systems. High-dimensional scenarios are usually tackled with scan-statistics and related methods, requiring stringent modeling assumptions for proper calibration. In this work we take a non-parametric stance, and propose a permutation-based variant of the higher criticism statistic not requiring knowledge of the null distribution. This results in an exact test in...
1 citation reported since publication in 2022.
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