American University
Browse
thesesdissertations_152_OBJ.pdf (22 MB)

A DATA ANALYTIC TOOL FOR MEASURING COMPOSITIONAL VARIABILITY

Download (22 MB)
thesis
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

American University

Notes

Degree awarded: Ph.D. Mathematics and Statistics. American University

Handle

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

Degree grantor

American University. Department of Mathematics and Statistics

Degree level

  • Doctoral

Submission ID

10055

Usage metrics

    Theses and Dissertations

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC