Publication
Title
Measuring overlap in binary regression
Author
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.
Language
English
Source (journal)
Computational statistics and data analysis. - Amsterdam
Publication
Amsterdam : 2001
ISSN
0167-9473
Volume/pages
37:1(2001), p. 65-75
ISI
000170079000005
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 03.01.2013
Last edited 13.07.2017
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