Sequential nonparametric tests for a change in distribution: an application to detecting radiological anomalies
University of Michigan School of Public Health
3755 SPH I, 1415 Washington Heights Ann Arbor, MI 48109-2029
In this talk I will 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. I 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. I 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. I will also briefly mention some of my other research directions. Light refreshments for seminar guests will be served at 3:10 p.m. in 3755 Department of Biostatistics

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

Oscar-Hernan Madrid-Padilla, PhD, Neyman Visiting Assistant Professor - Department of Statistics - University of California - Berkeley

icon to add this event to your google calendarFebruary 7, 2019
3:30 pm - 5:00 pm
3755 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029
Sponsored by: Department of Biostatistics
Contact Information: ZHenke Wu (zhenkewu@umich.edu) & Peisong Han (peisong@umich.edu

In this talk I will 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. I 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. I 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. I will also briefly mention some of my other research directions. Light refreshments for seminar guests will be served at 3:10 p.m. in 3755