Multivariate Outlier Identification Based on Robust Estimators of Location and Scatter

  • Claudia Becker
  • Steffen Liebscher
  • Thomas Kirschstein


Real-life data often contain some observations not consistent with the main bulk of the rest. Since classical statistical procedures often react sensitive against so-called outliers, the use of outlier identification methods based on robust statistical estimators is recommended. One class of such robust estimators is constructed according to the principle of subset selection, meaning that an outlier-free subset of the data is identified first which can then be used to discard or downweight deviating observations in order to robustly estimate the parameters of interest. Such approaches also deliver outlier identification methods. The general approach is presented and three methods are discussed which are developed especially for cases where there are no special restrictions on the data structure given by the main bulk of the observations.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Claudia Becker
    • 1
  • Steffen Liebscher
    • 1
  • Thomas Kirschstein
    • 1
  1. 1.Martin-Luther-University Halle-WittenbergHalleGermany

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