Robust Inference in the Logistic Regression Model

Rocca, Michele La

doi:10.1007/978-3-642-59471-7_26

Michele La Rocca⁸

Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization ((STUDIES CLASS))

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Abstract

Empirical likelihood is extended to a class of robust estimators for the parameter vector of the logistic regression model so to improve on both the known inference procedures based on empirical likelihood, which are not robust, and the usual robust inference procedures based on the normal approximation

The paper is supported by MURST 98 grant ex 60% 98.

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Authors and Affiliations

Università degli Studi di Salerno, Italy
Michele La Rocca

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Michele La Rocca
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Editors and Affiliations

Department SEFEMEQ, University of Rome “Tor Vergata”, Via di Tor Vergata, 00133, Rome, Italy
Simone Borra
Department SEGeS, University of Molise, Via de Sanctis, 86100, Campobasso, Italy
Roberto Rocci
Department Statistica, Probabilità e Statistiche Applicate, University of Rome “La Sapienza”, Piazza le Aldo Moro, 00185, Rome, Italy
Maurizio Vichi
University of Mannheim, 68131, Mannheim, Germany
Martin Schader (Lehrstuhl für Wirtschaftsinformatik III Schloß) (Lehrstuhl für Wirtschaftsinformatik III Schloß)

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Rocca, M.L. (2001). Robust Inference in the Logistic Regression Model. In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_26

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DOI: https://doi.org/10.1007/978-3-642-59471-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41488-9
Online ISBN: 978-3-642-59471-7
eBook Packages: Springer Book Archive

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