DISTRIBUTED NONPARAMETRIC INFERENCE USING A ONE-SAMPLE BOOTSTRAPPED ANDERSON-DARLING TEST and P-VALUE FUSION

Topi Halme, Visa Koivunen

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

Abstract

In this paper a method for distributed detection for scenarios when there is no explicit knowledge of the probability models associated with the hypotheses is proposed. The underlying distributions are accurately learned from the data by bootstrapping. We propose using a nonparametric one-sample Anderson-Darling test locally at each sensor. The one-sample version of the test gives superior performance in comparison to the two-sample alternative. The local decision statistics, in particular p-values are then sent to a fusion center to make the final decision. This allows for fusing local independent test statistics even if they obey different distributions at each sensor. Three different methods of fusing p-vales from independent tests are considered. Our simulation results demonstrate that p-value fusion is a powerful approach, especially when the Fisher's method is employed.

Original languageEnglish
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
PublisherIEEE
Pages56-60
Number of pages5
ISBN (Print)9781538644102
DOIs
Publication statusPublished - 17 Aug 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Data Science Workshop - Lausanne, Switzerland
Duration: 4 Jun 20186 Jun 2018

Workshop

WorkshopIEEE Data Science Workshop
Abbreviated titleDSW
CountrySwitzerland
CityLausanne
Period04/06/201806/06/2018

Fingerprint Dive into the research topics of 'DISTRIBUTED NONPARAMETRIC INFERENCE USING A ONE-SAMPLE BOOTSTRAPPED ANDERSON-DARLING TEST and P-VALUE FUSION'. Together they form a unique fingerprint.

Cite this