The application of generalized ridge and stein-like general minimax rules to multicollinear data

Abstract

We consider the use of minimax shrinkage estimators for the linear regression mcjel under several loss functions when severe multicollinearity is present. The examples considered illustrate that little or no departure from the least squares estimates is permitted in many cases when the data is highly multicollinear and/or shrinkage is toward a point in the parameter space that does not closely agree with the sample data