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Equivalence testing for mean vectors of multivariate normal populations

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dc.contributor.advisor Hu, Xiaomi
dc.contributor.author Clarkson, Elizabeth
dc.date.accessioned 2010-11-30T16:09:34Z
dc.date.available 2010-11-30T16:09:34Z
dc.date.copyright 2010
dc.date.issued 2010-05
dc.identifier.other d10002
dc.identifier.uri http://hdl.handle.net/10057/3280
dc.description Thesis (Ph.D.)--Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics and Statistics en
dc.description.abstract This 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.extent xii, 75 p. en
dc.format.extent 1063987 bytes
dc.format.extent 1843 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso en_US en
dc.publisher Wichita State University en
dc.rights Copyright Elizabeth Clarkson, 2010. All rights reserved en
dc.subject.lcsh Electronic dissertations en
dc.title Equivalence testing for mean vectors of multivariate normal populations en
dc.type Dissertation en

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