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• W4282967003 abstract "We proposed new two-parameter estimators to solve the problem called multicollinearity for the logistic regression model in this paper. We have derived these estimators’ properties and using the mean squared error (MSE) criterion; we compare theoretically with some of existing estimators, namely the maximum likelihood, ridge, Liu estimator, Kibria-Lukman, and Huang estimators. Furthermore, we obtain the estimators for k and d. A simulation is conducted in order to compare the estimators' performances. For illustration purposes, two real-life applications have been analyzed, that supported both theoretical and a simulation. We found that the proposed estimator, which combines the Liu estimator and the Kibria-Lukman estimator, has the best performance." @default.
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• W4282967003 date "2022-06-16" @default.
• W4282967003 modified "2023-09-27" @default.
• W4282967003 title "New Two-Parameter Estimators for the Logistic Regression Model with Multicollinearity" @default.
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• W4282967003 doi "https://doi.org/10.37394/23206.2022.21.48" @default.
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