Estimations of Kibria-Lukman and Lui to identify the most important variables influencing COVID-19

The problem of Multicollinearity between explanatory variables is one of the problems that have attracted the attention of many researchers and that the ordinary least squares method is unable to solve it , so the researchers found solutions to this problem by using the principal components method, the ridge regression method, or the lasso regression method, but these methods are not accurate enough to obtain on efficient estimates of the multiple regression model, especially in the presence of the problem of Multicollinearity. In this research, the capabilities of Kibria-Lukman and Lui will be used to determine the most important factors affecting infection with the Covid-19 virus among the variables that suffer from the problem of Multicollinearity to solve this problem and then obtain the best variables that represent the most important factors affecting infection with the virus. It was concluded that the Kibria-Lukman estimator is better than the rest of the estimators because it achieved the least mean squares error (MSE), and was able to solve the problem of Multicollinearity among the explanatory variables, followed by the Liu estimator and then the Ridge regression estimator, and finally the ordinary least squares estimator that was unable to solve the problem of linear multiplicity.