Sequential Nonparametric Tests for a Change in Distribution: An Application to Detecting Radiological Anomalies
成果类型：
Article
署名作者：
Padilla, Oscar Hernan Madrid; Athey, Alex; Reinhart, Alex; Scott, James G.
署名单位：
University of California System; University of California Berkeley; University of Texas System; University of Texas Austin; Carnegie Mellon University; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2018.1476245
发表日期：
2019
页码：
514-528
关键词：
摘要：
We propose a sequential nonparametric test for detecting a change in distribution, based on windowed Kolmogorov-Smirnov statistics. The approach is simple, robust, highly computationally efficient, easy to calibrate, and requires no parametric assumptions about the underlying null and alternative distributions. We show that both the false-alarm rate and the power of our procedure are amenable to rigorous analysis, and that the method outperforms existing sequential testing procedures in practice. We then apply the method to the problem of detecting radiological anomalies, using data collected from measurements of the background gamma-radiation spectrum on a large university campus. In this context, the proposed method leads to substantial improvements in time-to-detection for the kind of radiological anomalies of interest in law-enforcement and border-security applications.Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.