On the optimality of likelihood ratio test for prospect theory-based binary hypothesis testing

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buir.contributor.authorGezici, Sinan
dc.citation.epage1849en_US
dc.citation.issueNumber12en_US
dc.citation.spage1845en_US
dc.citation.volumeNumber25en_US
dc.contributor.authorGezici, Sinanen_US
dc.contributor.authorVarshney, P. K.en_US
dc.date.accessioned2019-02-21T16:04:42Z
dc.date.available2019-02-21T16:04:42Z
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn this letter, the optimality of the likelihood ratio test (LRT) is investigated for binary hypothesis testing problems in the presence of a behavioral decision-maker. By utilizing prospect theory, a behavioral decision-maker is modeled to cognitively distort probabilities and costs based on some weight and value functions, respectively. It is proved that the LRT may or may not be an optimal decision rule for prospect theory-based binary hypothesis testing, and conditions are derived to specify different scenarios. In addition, it is shown that when the LRT is an optimal decision rule, it corresponds to a randomized decision rule in some cases; i.e., nonrandomized LRTs may not be optimal. This is unlike Bayesian binary hypothesis testing, in which the optimal decision rule can always be expressed in the form of a nonrandomized LRT. Finally, it is proved that the optimal decision rule for prospect theory-based binary hypothesis testing can always be represented by a decision rule that randomizes at most two LRTs. Two examples are presented to corroborate the theoretical results.
dc.description.provenanceMade available in DSpace on 2019-02-21T16:04:42Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipManuscript received August 13, 2018; revised October 5, 2018; accepted October 16, 2018. Date of publication October 22, 2018; date of current version November 5, 2018. The work of P. K. Varshney was supported by Air Force Office of Scientific Research under Grant FA9550-17-1-0313 under the DDDAS program. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Ashish Pandharipande. (Corresponding author: Sinan Gezici.) S. Gezici is with the Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey (e-mail:,gezici@ee.bilkent.edu.tr).
dc.identifier.doi10.1109/LSP.2018.2877048
dc.identifier.issn1070-9908
dc.identifier.urihttp://hdl.handle.net/11693/50204
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://doi.org/10.1109/LSP.2018.2877048
dc.relation.projectBilkent Üniversitesi - Air Force Office of Scientific Research, AFOSR: FA9550-17-1-0313
dc.source.titleIEEE Signal Processing Lettersen_US
dc.subjectDetectionen_US
dc.subjectHypothesis testingen_US
dc.subjectLikelihood ratio testen_US
dc.subjectProspect theoryen_US
dc.subjectRandomizationen_US
dc.titleOn the optimality of likelihood ratio test for prospect theory-based binary hypothesis testingen_US
dc.typeArticleen_US

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