The College of Administration and Economics at the University of Baghdad discussed , a master’s thesis in field of Statistics by the student (Mevan Shakour Abdullah ) and tagged with ( A Restricted Gamma Ridge Regression Estimator Under Multicollinearity Problem with Application ) , Under supervision of (Assist.Prof. Suhail Najim Abood)
The Gamma Ridge Regression model is considered a non-linear regression model when the dependent variable does not follow a normal distribution. It differs from linear regression in that the value of μ_(i )for the dependent variable Y is replaced by a link function, and the purpose of using this function is to reduce the contrast value and make it more stable.
The problem of multicollinearity between explanatory variables is considered one of the most common problems facing researchers and negatively affects the estimated parameters of the model in regression analysis, in addition to the presence of restrictions imposed on the parameters that directly affect the estimation of the model parameters.
In this thesis, the researcher dealt with estimating the parameters of the restricted gamma ridge regression model in the presence of the multicollinearity problem by using four estimation methods and these are Maximum Likelihood method (MLE), Gamma Ridge Regression method (GRR), Restricted Maximum Likelihood method (RMLE), and the Built-in Estimator (RGRRE). A Monte-Carlo simulation method was used to generate data that follows the Gamma Regression model with different sample sizes. 25,50,100,250 suffers from the problem of multicollinearity in light of other variable influencing factors, which are the degree of correlation, the number of explanatory variables, and subjecting the parameters to a linear constraint.