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dc.contributor.advisorSmith, William B.
dc.creatorDunn, Charles Leslie
dc.descriptionVita -- Texas A&M Universityen
dc.description.abstractThe discrimination problem consisting of classifying an nxl observation vector as coming from one of two multivariate normal distributions which differ both in their mean vectors and covariance matrices is considered. A search for the procedure which minimizes the expected cost of misclassification is conducted within the class of procedures based upon a certain combination of n independent univariate discriminations. An efficient search of this class of procedures is made by employing an algorithm of the implicit enumeration type used in integer programming. In the case of known population parameters, the independent discriminant functions and exact probabilities of misclassification are found. When the parameters are unknown, estimates are introduced for the coefficients of each univariate discriminant function and for the probabilities of misclassification. A jackknife procedure is introduced in order to improve the estimation process. The method is applied to the problem classifying a set of male twins as to whether they are monozygotic or dizygotic.en
dc.publisherTexas A&M University
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work. beyond the provision of Fair Use.en
dc.subjectMajor statisticsen
dc.subject.lcshCombinatorial enumeration problemsen
dc.subject.lcshCombinatorial identitiesen
dc.subject.lcshVector analysisen
dc.titleCombinatoric classification of multivariate normal observation vectorsen
dc.typeThesisen A&M Universityen of Philosophyen
dc.format.digitalOriginreformatted digitalen

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