Outlier detection in interval data

A. Pedro Duarte Silva*, Peter Filzmoser, Paula Brito

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


A multivariate outlier detection method for interval data is proposed that makes use of a parametric approach to model the interval data. The trimmed maximum likelihood principle is adapted in order to robustly estimate the model parameters. A simulation study demonstrates the usefulness of the robust estimates for outlier detection, and new diagnostic plots allow gaining deeper insight into the structure of real world interval data.

Original languageEnglish
Pages (from-to)785-822
Number of pages38
JournalAdvances in Data Analysis and Classification
Issue number3
Publication statusPublished - Sept 2018


  • Interval data
  • Mahalanobis distance
  • Outliers
  • Robust statistics


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