Title
Measuring overlap in binary regression Measuring overlap in binary regression
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Amsterdam ,
Subject
Mathematics
Computer. Automation
Source (journal)
Computational statistics and data analysis. - Amsterdam
Volume/pages
37(2001) :1 , p. 65-75
ISSN
0167-9473
ISI
000170079000005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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 or probit regression models. The overlap is the number of observations that would need to be removed to obtain complete or quasi-complete separation, i.e. the situation where the regression parameters are no longer identifiable and the maximum likelihood estimate does not exist. it turns out that the overlap is often quite small. The results are equally useful in linear discriminant analysis. (C) 2001 Elsevier Science B.V. All rights reserved.
E-info
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