Outlier Detection for Compositional Data Using Robust Methods

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Imputation of missing values for compositional data using classical and robust methods (2010, methodology): “Using the procedure of Filzmoser and Hron ( 2008 ) one can see that this observation already represents an outlier in the data set .”
robCompositions: An R‐package for Robust Statistical Analysis of Compositional Data (2011, methodology): “Furthermore, methods to improve the quality of compositional data sets are implemented, like outlier detection (Filzmoser et al., 2008), and imputation of missing values (Hron et al, 2010).”
Correlation Analysis for Compositional Data (2009, background): “In contrast to the Euclidean distance, the Mahalanobis distance (Mahalanobis 1936) is a distance measure that accounts for the covariance structure (see Filzmoser and Hron 2008 for its usage in the context of compositional data).”
On the Interpretation of Orthonormal Coordinates for Compositional Data (2011, methodology): “This concept works perfectly where object-oriented methods, such as outlier detection (Filzmoser and Hron 2008) or discriminant analysis (Filzmoser et al.”
Principal component analysis for compositional data with outliers (2009, methodology): “However, multivariate outlier detection in case of compositional data is again not trivial (see Filzmoser and Hron, 2008), and thus an approach that is robust by itself is preferable.”