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Robust inference with binary data

Victoria-Feser, Maria-Pia

In: Psychometrika, 2002, vol. 67, no. 1, p. 21-32

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    Titre
    • Robust inference with binary data
    Auteur
    • Victoria-Feser, Maria-Pia. Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Psychometrika, 2002, vol. 67, no. 1, p. 21-32. Springer-Verlag
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:319146
    Summary
    In this paper robustness properties of the maximum likelihood estimator (MLE) and several robust estimators for the logistic regression model when the responses are binary are analysed. It is found that the MLE and the classical Rao's score test can be misleading in the presence of model misspecification which in the context of logistic regression means either misclassification's errors in the responses, or extreme data points in the design space. A general framework for robust estimation and testing is presented and a robust estimator as well as a robust testing procedure are presented. It is shown that they are less influenced by model misspecifications than their classical counterparts. They are finally applied to the analysis of binary data from a study on breastfeeding