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Bianco, A.M. and Yohai, V.J. (1996) Robust Estimation in the Logistic Regression Model. Springer, New York, 17-34.
https://doi.org/10.1007/978-1-4612-2380-1_2

has been cited by the following article:

• TITLE: Weighted Maximum Likelihood Technique for Logistic Regression

  AUTHORS: Idriss Abdelmajid Idriss, Weihu Cheng, Yemane Hailu Fissuh

  KEYWORDS: Logistic Regression, Clean Model, Robust Estimation, Contaminated Model, Weighted Maximum Likelihood Technique

  JOURNAL NAME: Open Journal of Statistics, Vol.13 No.6, December 8, 2023

  ABSTRACT: In this paper, a weighted maximum likelihood technique (WMLT) for the logistic regression model is presented. This method depended on a weight function that is continuously adaptable using Mahalanobis distances for predictor variables. Under the model, the asymptotic consistency of the suggested estimator is demonstrated and properties of finite-sample are also investigated via simulation. In simulation studies and real data sets, it is observed that the newly proposed technique demonstrated the greatest performance among all estimators compared.