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A Hierarchical Bayesian Model for the Unmixing Analysis of Compositional Data subject to Unit-sum Constraints

Modeling of compositional data is emerging as an active area in statistics. It is assumed that compositional data represent the convex linear mixing of definite numbers of independent sources usually referred to as end members. A generic problem in practice is to appropriately separate the end members and quantify their fractions from compositional data subject to nonnegative and unit-sum constraints. A number of methods essentially related to polytope expansion have been proposed. However, these deterministic methods have some potential problems.
In this study, a hierarchical Bayesian model was formulated, and the algorithms were coded in MATLABĂ’. A test run using both a synthetic and real-word dataset yields scientifically sound and mathematically optimal outputs broadly consistent with other non-Bayesian methods. Also, the sensitivity of this model to the choice of different priors and structure of the covariance matrix of error were discussed.

Identiferoai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-3051
Date15 May 2015
CreatorsYu, Shiyong
PublisherScholarWorks@UNO
Source SetsUniversity of New Orleans
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceUniversity of New Orleans Theses and Dissertations

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