Comparison of the LEO Estimator Method and the Two-Parameter LEO Method in Estimating the Parameters of the Conway–Maxwell–Poisson Regression Model in the Presence of the Multicollinearity Problem
DOI:
https://doi.org/10.62933/xb53be21Keywords:
Conway-Maxwell-Poisson , Regression model, Liu estimator, New modified two-parameNer Liu estimator, The problem of Multicollinearity, Simulation Experiments,Abstract
The aim of the research is to compare the Leo method and the two-parameter Leo method in estimating the parameters of the Conway-Maxwell-Poisson regression model in the presence of the Multicollinearity problem, While Poisson regression serves as a standard tool for modeling the association between a count response variable and explanatory variables, it is well documented that this approach is limited by Poisson's assumption of equal dispersion of the data, The Conway–Maxwell–Poisson (COMP) regression model has established itself as a viable alternative for real count data that Accounting for over- or under-dispersion, COM-Poisson regression can flexibly model associations that include the discrete count response variable and covariates, Using the simulation method (Mont-Carlo) to generate data tracking the Conway-Maxwell-Poisson regression model, and these data suffer from the problem of linear multiplicity according to the influencing and variable factors, including (sample size, degree of correlation, different values of the dispersion parameter, number of explanatory variables) and the average squares of error were relied upon as a criterion for comparing the methods of estimating the parameters of the model, Through the results of the simulation, the superiority of the new modified Leo estimator with two parameters over the estimator Leo, In the future study, the Generalized Mutual Verification Standard (GCV) can be used to select the bias parameters of the new modified two-parameter Leo estimator (CPNMTPL) for greater efficiency, The results indicate that the number of publications is growing, and the management and business area is the one that contributes the most, with the countries that produce in co-authorship also providing the most publications.
References
Abonazel, M. R. (2023). New modified two-parameter Liu estimator for the Conway–Maxwell Poisson regression model. Journal of Statistical Computation and Simulation, 93(12), 1976–1996. https://doi.org/10.1080/00949655.2023.2166046
Abonazel, M. R., Awwad, F. A., Tag Eldin, E., Kibria, B. M. G., & Khattab, I. G. (2023). Developing a two-parameter Liu estimator for the COM–Poisson regression model: Application and simulation. Frontiers in Applied Mathematics and Statistics, 9. https://doi.org/10.3389/fams.2023.956963
Abonazel, M. R., Saber, A. A., & Awwad, F. A. (2023). Kibria–Lukman estimator for the Conway–Maxwell Poisson regression model: Simulation and applications. Scientific African, 19, e01553. https://doi.org/10.1016/j.sciaf.2023.e01553
Akram, M. N., Amin, M., Sami, F., Mastor, A. B., Egeh, O. M., & Muse, A. H. (2022). A New Conway Maxwell–Poisson Liu Regression Estimator—Method and Application. Journal of Mathematics, 2022(1), 1–23. https://doi.org/10.1155/2022/3323955
Daly, F., & Gaunt, R. E. (2016). The conway-maxwell-poisson distribution: Distributional theory and approximation. Alea (Rio de Janeiro), 13(2), 635–658. https://doi.org/10.30757/alea.v13-25
Hamood, A. J. (2019). Compared Some Estimators Ordinary Ridge Regression And Bayesian Ridge Regression With Practical Application. Baghdad - College of Administration And Economics.
Imoto, T. (2014). A generalized Conway–Maxwell–Poisson distribution which includes the negative binomial distribution. Applied Mathematics and Computation, 247, 824–834. https://doi.org/10.1016/j.amc.2014.09.052
Rasheed, H., Sadik, N., & Algamal, Z. (2022). Jackknifed Liu-type estimator in the Conway-Maxwell Poisson regression model. International Journal of Nonlinear Analysis and Applications, 13(1), 3153–3168. https://doi.org/10.22075/ijnaa.2022.6064
SABRI, H. M. (2013). A Comparison Of Parameters Estimation Methods For The Poisson Regression Model Under Existing Of multicollinearty Problem With Application. Baghdad - College of Administration And Economics.
Sami, F., Amin, M., Akram, M. N., Butt, M. M., & Ashraf, B. (2022). A modified one parameter Liu estimator for Conway-Maxwell Poisson response model. Journal of Statistical Computation and Simulation, 92(12), 2448–2466. https://doi.org/10.1080/00949655.2022.2037136
Sami, F., Amin, M., & Butt, M. M. (2022). On the ridge estimation of the Conway-Maxwell Poisson regression model with multicollinearity: Methods and applications. Concurrency and Computation: Practice and Experience, 34(1), e6477. https://doi.org/10.1002/cpe.6477
Tanış, C., & Asar, Y. (2024). Liu-type estimator in Conway–Maxwell–Poisson regression model: theory, simulation and application. Statistics, 58(1), 65–86. https://doi.org/10.1080/02331888.2023.2301326
Zhan, D., & Young, D. S. (2024). Finite Mixtures of Mean-Parameterized Conway–Maxwell–Poisson Regressions. Journal of Statistical Theory and Practice, 18(1), 8. https://doi.org/10.1007/s42519-023-00362-3
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nour Alaa Aldin Abdul moneim, Suhail Najim Abood (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
