Subcompositional coherence and a novel proportionality index of parts
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 frst defne a coherent system of functions with...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/408220 |
| Acceso en línea: | https://hdl.handle.net/2117/408220 https://dx.doi.org/10.57645/20.8080.02.7 |
| Access Level: | acceso abierto |
| Palabra clave: | Distribution (Probability theory) Probabilities Compositional data analysis Aitchison geometry Simplex Compositional parts Proportionality Dominance Correlation Probabilitats Àrees temàtiques de la UPC::Matemàtiques i estadística::Probabilitat Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant |
| Sumario: | 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 frst defne 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 defnition of a novel index of proportionality between parts. |
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