Title
Robustness against separation and outliers in logistic regression Robustness against separation and outliers in logistic 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
43(2003) :3 , p. 315-332
ISSN
0167-9473
ISI
000183973200003
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. It suffers from the problem that its parameters are not identifiable when there is separation in the space of the explanatory variables. In that case, existing fitting techniques fail to converge or give the wrong answer. To remedy this, a slightly more general model is proposed under which the observed response is strongly related but not equal to. the unobservable true response. This model will be called the hidden logistic regression model because the unobservable true responses are comparable to a hidden layer in a feedforward neural net. The maximum estimated likelihood estimator is proposed in this model. It is robust against separation, always exists, and is easy to compute. Outlier-robust estimation is also studied in this setting, yielding the weighted maximum estimated likelihood estimator. (C) 2002 Elsevier Science B.V. All rights reserved.
E-info
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