A Comparison between Methods for Estimating the Restricted Gamma Ridge Regression Model Using the Simulation
DOI:
https://doi.org/10.33095/1xrve980Keywords:
Restricted Gamma Ridge Regression (RGRR), Gamma Ridge Regression (GRR), Restricted Maximum Likelihood Estimator (RMLE), Shrinkage factor k, Mean Square Error (MSE).Abstract
In this paper, we discuss estimating the parameters of the restricted gamma ridge regression model by combining gamma ridge regression with restricted maximum likelihood. The characteristics of the new estimator and its superiority over the restricted gamma ridge regression estimator and restricted maximum likelihood will be identified, using several formulas for the shrinkage factor k, and it will also be Using the Monte Carlo simulation method to generate data that suffers from the problem of multicollinearity with different sizes (n=25,50,100,250) in light of other influential factors (degree of correlation, number of explanatory variables), and subjecting the parameters to linear restrictions, to get rid of the problem of multicollinearity in light of the subjection of the parameters to the model has linear constraints and the model parameters will be estimated using four estimation methods that rely on the mean square error (MSE) as a standard for comparison between the estimation methods, Through the results of the simulation experiment it was shown that the compound estimator method is the best way to estimate the parameters of the finite gamma regression model .
Paper type : Research paper
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