Abstract
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.
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Kuhnt, S., Pawlitschko, J. (2005). Outlier Identification Rules for Generalized Linear Models. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_20
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DOI: https://doi.org/10.1007/3-540-26981-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23221-6
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