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dc.contributor.authorZhang, Gangen
dc.contributor.authorDuffy, A. P.en
dc.contributor.authorOrlandi, A.en
dc.date.accessioned2017-05-03T10:39:53Z
dc.date.available2017-05-03T10:39:53Z
dc.date.issued2017-05-10
dc.identifier.citationZhang, G., Duffy, A. and Orlandi, A. (2017) Statistical Figures of Merit for the Feature Selective Validation Method; IEEE Transactions on EMC, 59 (5), pp. 1482-1489en
dc.identifier.urihttp://hdl.handle.net/2086/14133
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractThis paper presents point-by-point Feature Selective Validation (FSV) data as a continuous distribution function, rather than in the more usual confidence histogram form, and from that derives the mean, standard deviation, skewness and kurtosis. The increased information that this offers is shown by presenting again the data from three previous exercises to verify FSV performance against visual assessment but including the standard deviation, where it is demonstrated that more robust conclusions about FSV overall assessment can be provided. The implication of the use of statistical data within FSV for including uncertainty in data comparisons is discussed.en
dc.language.isoen_USen
dc.publisherIEEEen
dc.subjectFeature Selective Validationen
dc.subjectstatistical momentsen
dc.subjectvisual assessmenten
dc.subjectuncertaintyen
dc.titleStatistical Figures of Merit for the Feature Selective Validation Methoden
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TEMC.2017.2695658
dc.researchgroupEngineering and Physical Sciences Institute (EPsi)en
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderNational Science Foundation of Chinaen
dc.projectidGrant No. 51507041en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-04-16en
dc.researchinstituteInstitute of Engineering Sciences (IES)en


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