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dc.contributor.authorWang, Qun
dc.date.accessioned2014-03-03T21:27:27Z
dc.date.available2014-03-03T21:27:27Z
dc.date.issued2004-08
dc.identifier.otherwang_qun_200408_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms
dc.identifier.urihttp://hdl.handle.net/10724/22017
dc.description.abstractThe presence of missing or incomplete data is a ubiquitous problem in real world datasets. In the thesis, we apply multiple imputation procedures to analyze incomplete multivariate datasets. We consider a dataset that contains both continuous and categorical variables, all with some missing values. While investigating three other imputation methods, we propose a two-part combination model, which melds the general linear model and the logistic model together, to predict and impute the missing values. Based on R2 and half-bound criteria, we analyze the different effects on variability due to the proportion of data missing, due to the association structure of the missing data, and due to the imputation procedure used.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectmultiple imputation
dc.subjectmissing data
dc.subjectquantitative variables
dc.subjectcategorical variables
dc.subjectgeneral linear model
dc.subjectlogistic model
dc.subjectlog-linear model
dc.titleInvestigation of multiple imputation procedures in the presence of missing quantitative and categorical variables
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorJaxk Reeves
dc.description.committeeJaxk Reeves
dc.description.committeeTharuvai N. Sriram
dc.description.committeeXiangrong Yin


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