Anomaly Detection for a Large Number of Streams: A Permutation-Based Higher Criticism Approach
Anomaly Detection for a Large Number of Streams: A Permutation-Based Higher Criticism Approach
复制标题
大量流的异常检测：基于排列的更高批评方法
DOI：
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发表时间：
2020
影响因子：
3.7
通讯作者：
Edwin van den Heuvel
中科院分区：
文献类型：
--
作者：
Ivo V. Stoepker;R. Castro;E. Arias;Edwin van den Heuvel
摘要在从流行病学研究到复杂系统监测的各种应用中，观测大量数据流时的异常检测是必不可少的。高维场景通常用扫描统计和相关方法来处理，需要严格的建模假设来进行适当的校准。在这项工作中，我们采取非参数的立场，并提出了一个置换为基础的变种更高的批评统计不需要知识的零分布。这导致在有限样本中的精确检验，其在广泛的指数模型类中是渐近最优的。我们证明了在有限样本的功率损失是最小的甲骨文测试。此外，由于所提出的统计量不依赖于渐近近似，它通常比依赖于这种近似的流行变体更好。我们纳入了建议，以便该检测可在实践中易于应用，并证明其在监测批量生产制剂活性成分含量均匀度方面的适用性。本文的补充材料可在网上查阅。
Abstract 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 the null distribution. This results in an exact test in finite samples which is asymptotically optimal in the wide class of exponential models. We demonstrate the power loss in finite samples is minimal with respect to the oracle test. Furthermore, since the proposed statistic does not rely on asymptotic approximations it typically performs better than popular variants of higher criticism that rely on such approximations. We include recommendations such that the test can be readily applied in practice, and demonstrate its applicability in monitoring the content uniformity of an active ingredient for a batch-produced drug product. Supplementary materials for this article are available online.
影响因子：
5.4
作者：
Shaofeng Zou;Yingbin Liang;H. V. Poor;Xinghua Shi
通讯作者：
Shaofeng Zou;Yingbin Liang;H. V. Poor;Xinghua Shi
DOI：
10.1201/9781315222912
发表时间：
2019-10
期刊：
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影响因子：
--
作者：
L. Held;N. Hens;P. O’Neill;J. Wallinga
通讯作者：
L. Held;N. Hens;P. O’Neill;J. Wallinga
影响因子：
8.6
作者：
M. Kulldorff;Lan Huang;K. Konty
通讯作者：
M. Kulldorff;Lan Huang;K. Konty