Anomaly Detection for a Large Number of Streams: A Permutation-Based Higher Criticism Approach
大量数据流的异常检测：基于置换的高批评统计量方法
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 nonparametric stance, and propose a permutation-based variant of the higher criticism statistic not requiring knowledge of…