This paper addressed parameter estimation in the Poisson regression model in the presence of multicollinearity when it is surmised that the parameter vector is restricted to a linear subspace. To improve the efficiency of parameter estimation, we proposed the Stein-Liu and positive Stein-Liu strategies. The proposed estimators' asymptotic distributional biases and variances were derived, and their variances were compared. The performance of the proposed estimators was investigated through an extensive Monte Carlo simulation study. The suggested estimators were also applied to data from Swedish football. The results confirmed that the performances of our estimators were superior to the unrestricted Liu estimator. As an important result, the Stein-Liu estimators uniformly perform better than the unrestricted Liu estimator
Zandi, Z. and Bevrani, H. (2024). Improved parameters estimation in the multicollinear Poisson regression model based on Stein-Liu estimators. Journal of Statistical Modelling: Theory and Applications, 5(2), 77-95. doi: 10.22034/jsmta.2025.21347.1134
MLA
Zandi, Z. , and Bevrani, H. . "Improved parameters estimation in the multicollinear Poisson regression model based on Stein-Liu estimators", Journal of Statistical Modelling: Theory and Applications, 5, 2, 2024, 77-95. doi: 10.22034/jsmta.2025.21347.1134
HARVARD
Zandi, Z., Bevrani, H. (2024). 'Improved parameters estimation in the multicollinear Poisson regression model based on Stein-Liu estimators', Journal of Statistical Modelling: Theory and Applications, 5(2), pp. 77-95. doi: 10.22034/jsmta.2025.21347.1134
CHICAGO
Z. Zandi and H. Bevrani, "Improved parameters estimation in the multicollinear Poisson regression model based on Stein-Liu estimators," Journal of Statistical Modelling: Theory and Applications, 5 2 (2024): 77-95, doi: 10.22034/jsmta.2025.21347.1134
VANCOUVER
Zandi, Z., Bevrani, H. Improved parameters estimation in the multicollinear Poisson regression model based on Stein-Liu estimators. Journal of Statistical Modelling: Theory and Applications, 2024; 5(2): 77-95. doi: 10.22034/jsmta.2025.21347.1134
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