Authors: Christmann, Andreas
Rousseeuw, Peter J.
Title: Measuring overlap in logistic regression
Language (ISO): en
Abstract: 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.
Subject Headings: linear discriminant analysis
logistic regression
outliers
overlap
probit regression
regression depth
separation
URI: http://hdl.handle.net/2003/4960
http://dx.doi.org/10.17877/DE290R-15082
Issue Date: 1999
Provenance: Universitätsbibliothek Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
99_25.pdfDNB163.81 kBAdobe PDFView/Open
tr25-99.ps413.37 kBPostscriptView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.