Exploratory data analysis for interval compositional data

Karel Hron (), Paula Brito () and Peter Filzmoser
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Karel Hron: Palacký University
Paula Brito: Universidade do Porto

Advances in Data Analysis and Classification, 2017, vol. 11, issue 2, No 2, 223-241

Abstract: Abstract Compositional data are considered as data where relative contributions of parts on a whole, conveyed by (log-)ratios between them, are essential for the analysis. In Symbolic Data Analysis (SDA), we are in the framework of interval data when elements are characterized by variables whose values are intervals on $$\mathbb {R}$$ R representing inherent variability. In this paper, we address the special problem of the analysis of interval compositions, i.e., when the interval data are obtained by the aggregation of compositions. It is assumed that the interval information is represented by the respective midpoints and ranges, and both sources of information are considered as compositions. In this context, we introduce the representation of interval data as three-way data. In the framework of the log-ratio approach from compositional data analysis, it is outlined how interval compositions can be treated in an exploratory context. The goal of the analysis is to represent the compositions by coordinates which are interpretable in terms of the original compositional parts. This is achieved by summarizing all relative information (logratios) about each part into one coordinate from the coordinate system. Based on an example from the European Union Statistics on Income and Living Conditions (EU-SILC), several possibilities for an exploratory data analysis approach for interval compositions are outlined and investigated.

Keywords: Interval data; Symbolic data analysis; Aitchison geometry on the simplex; Orthonormal coordinates; Outlier detection; Principal component analysis; 62H25; 62H99 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11634-016-0245-y

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