Outlier identification rules for generalized linear models

S. Kuhnt, J. Pawlitschko

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


    Observations which seem to deviate strongly from the main part of the data may occur in every statistical analysis. These observations, usually labelled as outliers, may cause completely misleading results when using standard methods and may also contain information about special events or dependencies. We discuss outliers in situations where a generalized linear model is assumed as null model for the regular data and introduce rules for their identification. For the special cases of a loglinear Poisson model and a logistic regression model some one-step identifiers based on robust and non-robust estimators are proposed and compared.
    Original languageEnglish
    Title of host publicationInnovations in Classification, Data Science, and Information Systems (Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Cottbus, Germany, March 12–14, 2003), Part II
    EditorsD. Baier, K.D. Warnecke
    Place of PublicationBerlin
    Number of pages8
    ISBN (Electronic)978-3-540-26981-6
    ISBN (Print)3-540-23221-4, 978-3-540-23221-6
    Publication statusPublished - 2005

    Publication series

    NameStudies in Classification, Data Analysis, and Knowledge Organization


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