2026-04-17T02:20:25Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/4717082024-12-17T14:23:56Zcom_2072_98col_2072_378192

00925njm 22002777a 4500 dc Egozcue, Juan José author Pawlowsky-Glahn, Vera author 2023 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. http://hdl.handle.net/2072/471708 Compositional data analysis Aitchison geometry Simplex Compositional parts Proportionality Dominance Correlation Subcompositional coherence and and a novel proportionality index of parts