Mathematical Geosciences

, Volume 50, Issue 3, pp 299–315 | Cite as

Means and Covariance Functions for Geostatistical Compositional Data: an Axiomatic Approach

Article
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Abstract

This work focuses on the characterization of the central tendency of a sample of compositional data. It provides new results about theoretical properties of means and covariance functions for compositional data, with an axiomatic perspective. Original results that shed new light on geostatistical modeling of compositional data are presented. As a first result, it is shown that the weighted arithmetic mean is the only central tendency characteristic satisfying a small set of axioms, namely continuity, reflexivity, and marginal stability. Moreover, this set of axioms also implies that the weights must be identical for all parts of the composition. This result has deep consequences for spatial multivariate covariance modeling of compositional data. In a geostatistical setting, it is shown as a second result that the proportional model of covariance functions (i.e., the product of a covariance matrix and a single correlation function) is the only model that provides identical kriging weights for all components of the compositional data. As a consequence of these two results, the proportional model of covariance function is the only covariance model compatible with reflexivity and marginal stability.

Keywords

Aitchison geometry Central tendency Functional equation Geostatistics Multivariate covariance function model 

Notes

Acknowledgements

We are truly indebted to one Advisory Editor and to the Editor-in-Chief for their very constructive comments, which helped to improve this paper.

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Copyright information

© International Association for Mathematical Geosciences 2017

Authors and Affiliations

  1. 1.Biostatistics and Spatial Processes, BioSP, INRAAvignonFrance
  2. 2.Ghent UniversityGhentBelgium

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