Minimum distance estimation for the logistic regression model
AbstractIt is well known that the maximum likelihood fit of the logistic regression parameters can be greatly affected by atypical observations. Several robust alternatives have been proposed. However, if we consider the model from the case-control viewpoint, it is clear that current techniques can exhibit poor behaviour in many common situations. A new robust class of estimation procedures is introduced. The estimators are constructed via a minimum distance approach after identifying the model with a semiparametric biased sampling model. The approach is developed under the case-control sampling scheme, yet is shown to be applicable under prospective sampling as well. A weighted Cramer--von Mises distance is used as an illustrative example of the methodology. Copyright 2005, Oxford University Press.
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Bibliographic InfoArticle provided by Biometrika Trust in its journal Biometrika.
Volume (Year): 92 (2005)
Issue (Month): 3 (September)
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- Croux, Christophe & Haesbroeck, G & Joossens, Kristel, 2005. "Logistic discrimination using robust estimators," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/122217, Katholieke Universiteit Leuven.
- Croux, Christophe & Haesbroeck, G & Joossens, Kristel, 2008. "Logistic discrimination using robust estimators: An influence function approach," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/175147, Katholieke Universiteit Leuven.
- Bianco, Ana M. & Martínez, Elena, 2009. "Robust testing in the logistic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4095-4105, October.
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