Mohamed R. Abonazel
Associate Professor of Applied Statistics and Econometrics at Cairo University
5 Dr. Ahmed Zewail Street, Postal Code: 12613, Orman, Giza, Egypt (email)
Generalized two-parameter estimators in the multinomial logit regression model: methods, simulation and application
- Citation:
- Farghali, R. A., M. Qasim, G. B. M. Kibria, and M. R. Abonazel, "Generalized two-parameter estimators in the multinomial logit regression model: methods, simulation and application", Communications in Statistics - Simulation and Computation: Taylor & Francis, pp. 1-16, 2021.
Abstract:
AbstractIn this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended for practitioners.
Notes:
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