Weighted Maximum Likelihood Technique for Logistic Regression

Idriss, Idriss Abdelmajid and Cheng, Weihu and Hailu, Yemane (2023) Weighted Maximum Likelihood Technique for Logistic Regression. Open Journal of Statistics, 13 (06). pp. 803-821. ISSN 2161-718X

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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.

Item Type: Article
Subjects: GO for STM > Mathematical Science
Depositing User: Unnamed user with email support@goforstm.com
Date Deposited: 13 Dec 2023 11:49
Last Modified: 13 Dec 2023 11:49
URI: http://archive.article4submit.com/id/eprint/2514

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