Author:
Rashad Nadwa Khazaal,Hammood Nawal Mahmood,Algamal Zakariya Yahya
Abstract
Abstract
The ridge estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The negative binomial regression model (NBRM) is a well-known model in application when the response variable is a count data with overdispersion. However, it is known that the variance of maximum likelihood estimator (MLE) of the NBRM coefficients can negatively affected in the presence of multicollinearity. In this paper, the generalized ridge estimator is proposed to overcome the limitation of ridge estimator. Several methods for estimating the shrinkage matrix have been adapted. Our Monte Carlo simulation results suggest that the proposed estimator, regardless the type of estimating method of shrinkage matrix is better than the MLE estimator and ridge estimator, in terms of MSE. In addition, some estimating method of shrinkage matrix can bring significant improvement relative to others.
Subject
General Physics and Astronomy
Reference27 articles.
1. Diagnostic in poisson regression models;Algamal;Electronic Journal of Applied Statistical Analysis,2012
2. Improved two-parameter estimators for the negative binomial and Poisson regression models;Kandemir Çetinkaya;Journal of Statistical Computation and Simulation,2019
3. A Simulation Study of Some Biasing Parameters for the Ridge Type Estimation of Poisson Regression;Kibria;Communications in Statistics-Simulation and Computation,2015
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