A study on Ridge, Liu and LASSO estimator in linear regression

In multiple linear regression analysis, the Ridge and Liu regression estimators are often used to solve the multicollinearity problem. In this thesis, Ridge, Liu methods and LASSO method, which is one of the most widely used biased estimators in the literature in case of multiple connections in linear regression, are discussed in detail. In addition, robust parameter estimation values were compared with classical methods in these methods. The MSE of the methods were compared and the results were interpreted through numerical examples where multicollinearity and outlier problems were seen together.