Outlier detection for compositional data using robust methods. (English) Zbl 1135.62040

Summary: Outlier detection based on the Mahalanobis distance (MD) requires an appropriate transformation in the case of compositional data. For the family of log-ratio transformations (additive, centered and isometric log-ratio transformations) it is shown that the MDs based on classical estimates are invariant to these transformations, and that the MDs based on affine equivariant estimators of location and covariance are the same for additive and isometric log-ratio transformations. Moreover, for three-dimensional compositions the data structure can be visualized by contour lines. In higher dimension the MDs of closed and opened data give an impression of the multivariate data behavior.


62H05 Characterization and structure theory for multivariate probability distributions; copulas
62F35 Robustness and adaptive procedures (parametric inference)
86A32 Geostatistics


robustbase; R
Full Text: DOI


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