The identification of multiple outliers

P.L. Davies, U. Gather

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    Abstract

    One approach to identifying outliers is to assume that the outliers have a different distribution from the remaining observations. In this article we define outliers in terms of their position relative to the model for the good observations. The outlier identification problem is then the problem of identifying those observations that lie in a so-called outlier region. Methods based on robust statistics and outward testing are shown to have the highest possible breakdown points in a sense derived from Donoho and Huber. But a more detailed analysis shows that methods based on robust statistics perform better with respect to worst-case behavior. A concrete outlier identifier based on a suggestion of Hampel is given.
    Original languageEnglish
    Pages (from-to)782-792
    Number of pages11
    JournalJournal of the American Statistical Association
    Volume88
    Issue number423
    DOIs
    Publication statusPublished - 1993

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