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Detection of a random alteration in a multivariate observation when knowing probable direction

dc.contributor.authorKatz, Barry P.en_US
dc.contributor.authorBrown, Morton B.en_US
dc.date.accessioned2006-04-07T20:22:28Z
dc.date.available2006-04-07T20:22:28Z
dc.date.issued1988-03en_US
dc.identifier.citationKatz, Barry P., Brown, Morton B. (1988/03)."Detection of a random alteration in a multivariate observation when knowing probable direction." Computational Statistics &amp; Data Analysis 6(2): 145-155. <http://hdl.handle.net/2027.42/27380>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6V8V-45DHTHW-46/2/5dca09cfb129aede40a1d2db154d6ba2en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/27380
dc.description.abstractAn observation from a multivariate distribution may be subject to perturbation in a known subset of the variables. A likelihood radio statistic is developed to test whether or not there has been an addition of a random quantity in a prespecified direction to an observation from a multivariate normal distribution. When the variance of this addition is unknown, a secondarily Bayes approach is used to eliminate this variance which acts as a nuisance parameter. The testing procedure is based on a distribution-free tolerance interval.en_US
dc.format.extent803563 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleDetection of a random alteration in a multivariate observation when knowing probable directionen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationotherRegenstrief Institute and Department of Medicine, Indiana University, Indianapolis, IN, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/27380/1/0000409.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0167-9473(88)90045-Xen_US
dc.identifier.sourceComputational Statistics &amp; Data Analysisen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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