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Advisor(s)
Abstract(s)
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.
Description
Keywords
Outliers Robust statistics Interval data Mahalanobis distance
Pedagogical Context
Citation
Duarte Silva, A. P., Filzmoser, P., Brito, P. (2017). Outlier detection in interval data. Advances in Data Analysis and Classification, 12 (3), 785-822
Publisher
Springer Verlag