Exploratory tools for outlier detection in compositional data with structural zeros

DOI10.1080/02664763.2016.1182135OpenAlexW2387269970MaRDI QIDQ5138577

Peter Filzmoser, Matthias Templ, Karel Hron

Publication date: 4 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2016.1182135

zbMATH Keywords

principal component analysiscovariance estimationMahalanobis distanceAitchison geometry on the simplexstructural zeros

Mathematics Subject Classification ID

Applied statistics (educational aspects) (97K80) Foundations and methodology of statistics (educational aspects) (97K70) Descriptive statistics (educational aspects) (97K40)

Related Items (6)

Evaluation of robust outlier detection methods for zero-inflated complex data ⋮ Weighting of parts in compositional data analysis: advances and applications ⋮ Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers ⋮ A multivariate test for detecting fraud based on Benford's law, with application to music streaming data ⋮ Analysing pairwise logratios revisited ⋮ Compositional scalar-on-function regression with application to sediment particle size distributions

Uses Software

• R
• laeken
• robCompositions
• impute
• simPop
• GitHub

Cites Work

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• Biplots of Compositional Data
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