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Gervini, D. (2005) Robust Adaptive Estimators for Binary Regression Models. Journal of Statistical Planning and Inference, 131, 297-311.
https://doi.org/10.1016/j.jspi.2004.02.006

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.