On the restricted Neyman-Pearson approach for composite hypothesis-testing in presence of prior distribution uncertainty

dc.citation.epage5065en_US
dc.citation.issueNumber10en_US
dc.citation.spage5056en_US
dc.citation.volumeNumber59en_US
dc.contributor.authorBayram, S.en_US
dc.contributor.authorGezici, Sinanen_US
dc.contributor.bilkentauthorGezici, Sinan
dc.date.accessioned2015-07-28T12:05:43Z
dc.date.available2015-07-28T12:05:43Z
dc.date.issued2011en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThe restricted Neyman–Pearson (NP) approach is studied for composite hypothesis-testing problems in the presence of uncertainty in the prior probability distribution under the alternative hypothesis. A restricted NP decision rule aims to maximize the average detection probability under the constraints on the worst-case detection and false-alarm probabilities, and adjusts the constraint on the worst-case detection probability according to the amount of uncertainty in the prior probability distribution. In this study, optimal decision rules according to the restricted NP criterion are investigated. Also, an algorithm is provided to calculate the optimal restricted NP decision rule. In addition, it is shown that the average detection probability is a strictly decreasing and concave function of the constraint on the minimum detection probability. Finally, a detection example is presented to investigate the theoretical results, and extensions to more generic scenarios are provided.en_US
dc.identifier.doi10.1109/TSP.2011.2153846en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/13323
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSP.2011.2153846en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectComposite hypothesisen_US
dc.subjectHypothesis-testingen_US
dc.subjectNeyman–Pearson (NP)en_US
dc.subjectRestricted Bayesen_US
dc.subjectMax-minen_US
dc.titleOn the restricted Neyman-Pearson approach for composite hypothesis-testing in presence of prior distribution uncertaintyen_US
dc.typeArticleen_US
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