Kunsch, H.R., Stefonski, L.A. and Carroll, R.J. (1989) Conditionally Unbiased Bounded-Influence Estimation in General Regression Models, with Applications to Generalized Linear Models. Journal of the American Statistical Association, 84, 460-466.
https://doi.org/10.1080/01621459.1989.10478791

has been cited by the following article:

• TITLE: The Performance of Robust Methods in Logistic Regression Model

  AUTHORS: Idriss Abdelmajid Idriss Ahmed, Weihu Cheng

  KEYWORDS: Logistic Regression, Maximum Likelihood Estimator, Robust Estimation, Outlier, Weighted Maximum Likelihood

  JOURNAL NAME: Open Journal of Statistics, Vol.10 No.1, February 28, 2020

  ABSTRACT: Logistic regression is the most important tool for data analysis in various fields. The classical approach for estimating parameters is the maximum likelihood estimation, a disadvantage of this method is high sensitivity to outlying observations. Robust estimators for logistic regression are alternative techniques due to their robustness. This paper presents a new class of robust techniques for logistic regression. They are weighted maximum likelihood estimators which are considered as Mallows-type estimator. Moreover, we compare the performance of these techniques with classical maximum likelihood and some existing robust estimators. The results are illustrated depending on a simulation study and real datasets.The new estimators showed the best performance relative to other estimators.