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dc.contributor.authorConnally, Mark R
dc.date.accessioned2014-03-03T23:07:54Z
dc.date.available2014-03-03T23:07:54Z
dc.date.issued2004-12
dc.identifier.otherconnally_mark_r_200412_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/connally_mark_r_200412_phd
dc.identifier.urihttp://hdl.handle.net/10724/22084
dc.description.abstractA Monte Carlo study was undertaken comparing the linear and quadratic discriminant functionin classifying individuals in two mulivariate nonmally distributed populations with unequalcovariance matrices. The conditions varied were: the covariance matrix differences, groupseparation, number of predictors, sample size, priors, and number of populations (groups). Theinternal error rate for both the linear and quadratic classification rule were compared. For allconditions, the quadratic classification rule performed better (i.e., had lower internal error rates)than the linear classification rule. The difference between the linear and quadratic classificationrules was smallest when the number of predictors was small and the variances were different.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectDiscriminant Analysis
dc.subjectUnequal covariance matrices
dc.subjectlinear
dc.subjectquadratic
dc.subjectcomparison of classification rules
dc.titleIdentifying covariance differences in comparisons of linear versus quadratic classification rule
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentEducational Psychology
dc.description.majorEducational Psychology
dc.description.advisorCarl J Huberty
dc.description.committeeCarl J Huberty
dc.description.committeeSeock-Ho Kim
dc.description.committeeStephen Olejnik
dc.description.committeeJaxk Reeves
dc.description.committeeJoseph M. Wisenbaker


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