Arbeitspapier
Measuring overlap in logistic regression
In this paper we show that the recent notion of regression depth can be used as a data-analytic tool to measure the amount of separation between successes and failures in the binary response framework. Extending this algorithm allows us to compute the overlap in data sets which are commonly fitted by logistic regression models. The overlap is the number of observations that would need to be removed to obtain complete or quasicomplete separation, i.e. the situation where the logistic regression parameters are no longer identifiable and the maximum likelihood estimate does not exist. It turns out that the overlap is often quite small.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 1999,25
- Subject
-
Linear discriminant analysis
Logistic regression
Outliers
Overlap
Probit regression
Regression depth
Separation
- Event
-
Geistige Schöpfung
- (who)
-
Christmann, Andreas
Rousseeuw, Peter J.
- Event
-
Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
-
Dortmund
- (when)
-
1999
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Christmann, Andreas
- Rousseeuw, Peter J.
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 1999
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