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Unsupervised outlier detection for compositional data

Author

Listed:
  • Divino, Fabio
  • Kärkkäinen, Salme
  • Maruotti, Antonello

Abstract

This paper proposes an unsupervised method for outlier detection in compositional data via an appropriate transformation and modeling with a contaminated normal distribution. Parameters are estimated using an Expectation-Conditional-Maximization algorithm. Simulation studies and real-data applications demonstrate the method’s robustness and superior performance compared to three existing alternatives.

Suggested Citation

  • Divino, Fabio & Kärkkäinen, Salme & Maruotti, Antonello, 2026. "Unsupervised outlier detection for compositional data," Statistics & Probability Letters, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:stapro:v:227:y:2026:i:c:s0167715225001555
    DOI: 10.1016/j.spl.2025.110510
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    References listed on IDEAS

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    1. Anne Ruiz-Gazen & Christine Thomas-Agnan & Thibault Laurent & Camille Mondon, 2023. "Detecting Outliers in Compositional Data Using Invariant Coordinate Selection," Springer Books, in: Mengxi Yi & Klaus Nordhausen (ed.), Robust and Multivariate Statistical Methods, pages 197-224, Springer.
    2. Salvatore D. Tomarchio & Antonio Punzo & Johannes T. Ferreira & Andriette Bekker, 2025. "A New Look at the Dirichlet Distribution: Robustness, Clustering, and Both Together," Journal of Classification, Springer;The Classification Society, vol. 42(1), pages 31-53, March.
    3. M. A. Di Palma & M. Gallo, 2016. "A co-median approach to detect compositional outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(13), pages 2348-2362, October.
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