Mathematical Geosciences

, Volume 40, Issue 3, pp 233–248 | Cite as

Outlier Detection for Compositional Data Using Robust Methods



Outlier detection based on the Mahalanobis distance (MD) requires an appropriate transformation in case of compositional data. For the family of logratio transformations (additive, centered and isometric logratio transformation) 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 logratio transformation. Moreover, for 3-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.


Mahalanobis distance Robust statistics Ternary diagram Multivariate outliers Logratio transformation 


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Copyright information

© International Association for Mathematical Geology 2008

Authors and Affiliations

  1. 1.Dept. of Statistics and Probability TheoryVienna University of TechnologyViennaAustria
  2. 2.Dept. of Mathematical Analysis and Applications of MathematicsPalacký University OlomoucOlomoucCzech Republic

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