Asymptotically achievable error probabilities for multiple hypothesis testing

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

Abstract

The region of achievable error probabilities for k-ary hypothesis tests is studied in the asymptotic setting with n independent and identically distributed observations. We identify a k2 - k - 1 dimensional parametric family of optimal (non-dominated) tests and relate it to a family of Bayes tests whose loss function depends exponentially on n. We asymptotically characterize the conditional error probabilities for these tests within O(1) as n → ∞, using strong large deviations analysis.

Original languageEnglish (US)
Title of host publicationProceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1541-1545
Number of pages5
ISBN (Electronic)9781509018062
DOIs
StatePublished - Aug 10 2016
Event2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain
Duration: Jul 10 2016Jul 15 2016

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2016-August
ISSN (Print)2157-8095

Other

Other2016 IEEE International Symposium on Information Theory, ISIT 2016
CountrySpain
CityBarcelona
Period7/10/167/15/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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