Conditional Ridge Estimator And Its Properties For Coefficient Of Constraint Linear Regression Model

In the study of problems of mathematical statistics,parameter estimation of linear regression model has been the research hot spot.With the development of statistical theory,it was found that the least squares estimation in dealing with complex collinearity problem is insufficient,some biased estimates slowly become the research focus.For without the constraint of the estimates of parameters in the linear regression model has a systematic study.This article is based on constraint linear regression model to study parameter estimation.From the perspective of the constrained linear regression model,this paper first introduces the constraint least squares estimation.Emphatically from the angle of the biased estimation,gives some constraint biased estimation,such as conditional ridge estimation,constraint ridge Stein estimation.And focuses on the two conditional ridge estimation,in the end we attain both consistent.Then under the mean square error,ridge type Stein estimation is better than that constraint least squares estimation.