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Bianco, A.M. and Yohai, V.J. (1996) Robust Estimation in the Logistic Regression Model. In: Reider, H., Ed., Robust Statistics, Data Analysis, and Computer Intensive Methods, Springer, Berlin, 17-34.
https://doi.org/10.1007/978-1-4612-2380-1_2

has been cited by the following article:

• TITLE: Robust Estimators for Poisson Regression

  AUTHORS: Idriss Abdelmajid Idriss, Weihu Cheng

  KEYWORDS: Poisson Regression Model, Maximum Likelihood Estimator, Robust Estimation, Contaminated Model, Weighted Maximum Likelihood Estimator

  JOURNAL NAME: Open Journal of Statistics, Vol.13 No.1, February 28, 2023

  ABSTRACT: The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.