Research ArticleOpen AccessGoogle Scholar indexed
Weighted Maximum Likelihood Technique for Logistic Regression
Department of Statistics, Faculty of Science, Beijing University of Technology, Beijing, China
Department of Statistics, Faculty of Science, Beijing University of Technology, Beijing, China
Department of Statistics, Aksum University, Tigray, Ethiopia
- 1 Department of Statistics, Faculty of Science, Beijing University of Technology, Beijing, China
- 2 Department of Statistics, Faculty of Science, Beijing University of Technology, Beijing, China
- 3 Department of Statistics, Aksum University, Tigray, Ethiopia
Open Journal of Statistics·Volume 13 (2023)·Pages 803–821·Published 13 November 2023·DOI10.4236/ojs.2023.136041
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Abstract
In this paper, a weighted maximum likelihood technique (WMLT) for the lo gistic regression model is presented. This method depended on a weight function that is continuously adaptable using Mahalanobis distances for pre dictor 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.
KeywordsLogistic RegressionClean ModelRobust EstimationContaminated ModelWeighted Maximum Likelihood Technique
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