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A DATA ANALYTIC TOOL FOR MEASURING COMPOSITIONAL VARIABILITY

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posted on 2023-09-07, 02:02 authored by Nedaa M. Timraz

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.

History

Publisher

ProQuest

Language

English

Handle

http://hdl.handle.net/1961/11129

Committee chair

Robert W. Jernigan

Committee member(s)

Elizabeth Malloy; Monica Jackson

Degree discipline

Statistics

Degree grantor

American University. Department of Mathematics and Statistics

Degree level

  • Doctoral

Degree name

Ph.D. in Statistics, American University, 2011

Local identifier

thesesdissertations_152_OBJ.pdf

Media type

application/pdf

Pagination

151 pages

Call number

Thesis 9698

MMS ID

99129782213604102

Submission ID

10055

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