The Research On The Properties Of Two-Parameter Estimator In The Linear Regression Model

The research for parameter estimator in the linear regression model has been a hot part in statistics.In the past research,the main method is the least square estimator.But,the least square estimator is in inadequate in the treatment of the multicollinearity problems.Many biased estimators have been seriously rise.The biased estimator is one of the most direct methods to reduce the multicollinearity.Many of the biased estimators,some are influential,such as Two-Parameter Estimator,Stein Estimator,James-Stein Shrinkage Estimator,Liu Estimator and so on.In this paper,we study a simple linear regression model.Considering the linear model under the condition of the unrestricted,we make a further discussion on the parameter estimator.The main content is as follows:First,it introduces some basic knowledge about the Linear Regression Model,Two-Parameter Estimator,Stein Estimator,the Least Square Estimator.And this paper mainly discusses the relationship between the Stein Estimator and the Two-Parameter Estimator.Then this paper discusses the Stein Estimator and the Two-Parameter Estimator under generalized mean square error and balanced loss function and gives the proof.