Compositional cubes: a new concept for multi-factorial compositions

Kamila Fačevicová (), Peter Filzmoser () and Karel Hron ()
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Kamila Fačevicová: Palacký University Olomouc
Peter Filzmoser: TU Wien
Karel Hron: Palacký University Olomouc

Statistical Papers, 2023, vol. 64, issue 3, No 9, 955-985

Abstract: Abstract 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.

Keywords: Analysis of independence; Compositional data; Coordinate representation; Orthogonal decomposition (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-022-01350-8

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