Outlier detection in interval data

Pedro Silva, Peter Filzmoser and Paula Brito
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Paula Brito: Universidade do Porto

Advances in Data Analysis and Classification, 2018, vol. 12, issue 3, No 15, 785-822

Abstract: Abstract 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.

Keywords: Outliers; Robust statistics; Interval data; Mahalanobis distance; 62-07 (Data Analysis); 62F35 (Robustness and adaptive procedures); 62H86 (Multivariate analysis and fuzziness) (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-017-0305-y

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