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Outlier identification rules for generalized linear models

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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. Therefore it is interest to identify them. We discuss outliers in situations where a generalized linear model is assumed as null-model for the regular data and introduce rules for their identifications. 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.

Language: Englisch

Bibliographic citation: Series: Technical Report ; No. 2003,12

Subject: Regression
Theorie
Statistischer Fehler

Event: Geistige Schöpfung

(who): Kuhnt, Sonja
Pawlitschko, Jörg

Event: Veröffentlichung

(who): Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen

(where): Dortmund

(when): 2003

Handle: http://hdl.handle.net/10419/49344

Last update: 10.03.2025, 11:41 AM CET

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Object type

Arbeitspapier

Associated

Kuhnt, Sonja
Pawlitschko, Jörg
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen

Time of origin

2003

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