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We propose a sequential nonparametric test for detecting a change indistribution, based on windowed Kolmogorov--Smirnov statistics. The approach issimple, robust, highly computationally efficient, easy to calibrate, andrequires no parametric assumptions about the underlying null and alternativedistributions. We show that both the false-alarm rate and the power of ourprocedure are amenable to rigorous analysis, and that the method outperformsexisting sequential testing procedures in practice. We then apply the method tothe problem of detecting radiological anomalies, using data collected frommeasurements of the background gamma-radiation spectrum on a large universitycampus. In this context, the proposed method leads to substantial improvementsin time-to-detection for the kind of radiological anomalies of interest inlaw-enforcement and border-security applications.

Sequential nonparametric tests for a change in distribution: an application to detecting radiological anomalies