A DATA ANALYTIC TOOL FOR MEASURING COMPOSITIONAL VARIABILITY

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A DATA ANALYTIC TOOL FOR MEASURING COMPOSITIONAL VARIABILITY

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dc.contributor.advisor Jernigan, Robert W en_US
dc.contributor.author Timraz, Nedaa Mostafa en_US
dc.contributor.other Malloy, Elizabeth en_US
dc.contributor.other Jackson, Monica en_US
dc.date.accessioned 2012-08-22T15:01:37Z
dc.date.available 2012-08-22T15:01:37Z
dc.date.created 2011 en_US
dc.date.issued 2012-08-22
dc.identifier.other Timraz_american_0008E_10055 en_US
dc.identifier.uri http://hdl.handle.net/1961/11129
dc.description Degree awarded: Ph.D. Mathematics and Statistics. American University en_US
dc.description.abstract Compositional data are non-negative proportions that sum to one. Under the unit-sum constraint, the standard statistical techniques devised for unconstrained variables can not be applied to analyze compositional data. Aitchison (1986) developed a method based on logratio transformations of compositional data that is widely used. This method is limited by the assumption of strictly positive components or the use of special treatments to accommodate possible zero components. We propose a new data analytic measure of compositional data variability based on the Sum of Coefficients of Variation to address a common objective in compositional data analysis to identify a subset of the variables that retains most of the variability of the full composition. In selecting these subcompositions, this new method resolves the difficulty of zeros in compositional data avoiding any special consideration of zeros. The new technique is investigated analytically and illustrated with real and simulated data sets. en_US
dc.format.extent 151 p. en_US
dc.format.mimetype application/pdf en_US
dc.publisher American University en_US
dc.subject Statistics en_US
dc.title A DATA ANALYTIC TOOL FOR MEASURING COMPOSITIONAL VARIABILITY en_US
dc.type Text en_US
dc.type Dissertation en_US


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