Universal multiple outlier hypothesis testing

Yun Li, Sirin Nitinawarat, Venugopal Varadachari Veeravalli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The universal multiple outlier hypothesis testing problem is studied in two settings. In the first setting, each outlier can be arbitrarily distributed, and the number of outliers is fixed and known. In the second setting, the number of outliers is unknown at the outset. Nothing is known about the typical and outlier distributions other than that they are different and have full supports. For the first setting, a universally exponentially consistent test is proposed, and its achievable error exponent is characterized. The limiting error exponent achieved by such test is analyzed as the number of coordinates goes to infinity, and it is shown that the test also enjoys universally asymptotically exponential consistency. For the second setting, it is shown that with the assumption of outliers being identically distributed and the exclusion of the null hypothesis, a test based on the generalize likelihood principle is universally exponentially consistent.

Original languageEnglish (US)
Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages177-180
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
Duration: Dec 15 2013Dec 18 2013

Publication series

Name2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Other

Other2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
CountryFrance
CitySaint Martin
Period12/15/1312/18/13

ASJC Scopus subject areas

  • Computer Science Applications

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  • Cite this

    Li, Y., Nitinawarat, S., & Veeravalli, V. V. (2013). Universal multiple outlier hypothesis testing. In 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 (pp. 177-180). [6714036] (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013). https://doi.org/10.1109/CAMSAP.2013.6714036