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dc.contributor.advisorHu, Xiaomi
dc.contributor.authorClarkson, Elizabeth
dc.date.accessioned2010-11-30T16:09:34Z
dc.date.available2010-11-30T16:09:34Z
dc.date.copyright2010
dc.date.issued2010-05
dc.identifier.otherd10002
dc.identifier.urihttp://hdl.handle.net/10057/3280
dc.descriptionThesis (Ph.D.)--Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics and Statisticsen
dc.description.abstractThis dissertation examines the problem of comparing samples of multivariate normal data from two populations and concluding whether the populations are equivalent; equivalence is defined as the distance between the mean vectors of the two samples being less than a given value. Test statistics are developed for each of two cases using the ratio of the maximized likelihood functions. Case 1 assumes both populations have a common known covariance matrix. Case 2 assumes both populations have a common covariance matrix, but this covariance matrix is a known matrix multiplied by an unknown scalar value. The power function and bias of each of the test statistics is evaluated. Tables of critical values are provided.en
dc.format.extentxii, 75 p.en
dc.format.extent1063987 bytes
dc.format.extent1843 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_USen
dc.publisherWichita State Universityen
dc.rightsCopyright Elizabeth Clarkson, 2010. All rights reserveden
dc.subject.lcshElectronic dissertationsen
dc.titleEquivalence testing for mean vectors of multivariate normal populationsen
dc.typeDissertationen


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