Compositional cubes: a new concept for multi-factorial compositions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F23%3A73621535" target="_blank" >RIV/61989592:15310/23:73621535 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00362-022-01350-8" target="_blank" >https://link.springer.com/article/10.1007/s00362-022-01350-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00362-022-01350-8" target="_blank" >10.1007/s00362-022-01350-8</a>

Result language
angličtina
Original language name
Compositional cubes: a new concept for multi-factorial compositions
Original language description
Compositional data are commonly known as multivariate observations carrying relative information. Even though the case of vector or even two-factorial compositional data (compositional tables) is already well described in the literature, there is still a need for a comprehensive approach to the analysis of multi-factorial relative-valued data. Therefore, this contribution builds around the current knowledge about compositional data a general theoretical framework for k-factorial compositional data. As a main finding it turns out that, similar to the case of compositional tables, also the multi-factorial structures can be orthogonally decomposed into an independent and several interactive parts and, moreover, a coordinate representation allowing for their separate analysis by standard analytical methods can be constructed. For the sake of simplicity, these features are explained in detail for the case of three-factorial compositions (compositional cubes), followed by an outline covering the general case. The three-dimensional structure is analyzed in depth in two practical examples, dealing with systems of spatial and time dependent compositional cubes. The methodology is implemented in the R package robCompositions.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GF22-15684L" target="_blank" >GF22-15684L: Generalized relative data and robustness in Bayes spaces</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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