Nonparametric detection using empirical distributions and bootstrapping

Martin Gölz, Visa Koivunen, Abdelhak Zoubir

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

5 Citations (Scopus)


This paper addresses the problem of decision making when there is no or very vague knowledge about the probability models associated with the hypotheses. Such scenarios occur for example in Internet of Things (IoT), environmental surveillance and data analytics. The probability models are learned from the data by empirical distributions that provide an accurate approximation of the true model. Hence, the approach is fully nonparametric. The bootstrap method is employed to approximate the distribution of the decision statistic. The actual test is based on the Anderson-Darling test that is shown to perform reliably even if the empirical distributions differ only slightly. The proposed detector allows controlling Type I and II error levels without specifying explicit probability models or performing tedious large sample analysis. It is also proved that the test can achieve the specified power. Numerical simulations validate the results.
Original languageEnglish
Title of host publication25th European Signal Processing Conference (EUSIPCO 2017)
Number of pages5
ISBN (Electronic)978-0-9928626-7-1
ISBN (Print)978-1-5386-0751-0
Publication statusPublished - Oct 2017
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Kos Island, Greece, Kos, Greece
Duration: 28 Aug 20172 Sept 2017
Conference number: 25

Publication series

Name European Signal Processing Conference
ISSN (Electronic)2076-1465


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
Internet address


  • signal processing
  • nonparametric methods


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