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.