Universitat Autònoma de Barcelona
Research in compositional data analysis was motivated by spurious (Pearson) correlation. Spurious results are due to semantic incoherence, but the question of ways to relate parts in a statistically consistent way remains open. To solve this problem, we first define a coherent system of functions with respect to a subcomposition and analyze the space of parts. This leads to understanding why measures like covariance and correlation depend on the subcomposition considered, while measures like the distance between parts are independent of the same. It allows the definition of a novel index of proportionality between parts.
Article
English
Compositional data analysis; Aitchison geometry; Simplex; Compositional parts; Proportionality; Dominance; Correlation
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SORT : statistics and operations research transactions ; Vol. 47 Núm. 2 (2023), p. 229-244
open access
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