Abstract
A new class of robust and Fisher-consistent M-estimates for the logistic regression models is introduced. We show that these estimates are consistent and asymptotically normal. Their robustness is studied through the computation of asymptotic bias curves under point-mass contamination for the case when the covariates follow a multivariate normal distribution. We illustrate the behavior of these estimates with two data sets. Finally, we mention some possible extensions of these M-estimates for a multinomial response.
This research was partially done while V.J. Yohai was visiting the Department of Statistics of the University of Washington supported by ONR Contract No. CN00014-91-J-1074. The authors thank Professor Raymond J. Carroll for providing the food stamp data set.
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© 1996 Springer-Verlag New York, Inc.
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Bianco, A.M., Yohai, V.J. (1996). Robust Estimation in the Logistic Regression Model. In: Rieder, H. (eds) Robust Statistics, Data Analysis, and Computer Intensive Methods. Lecture Notes in Statistics, vol 109. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2380-1_2
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DOI: https://doi.org/10.1007/978-1-4612-2380-1_2
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