Mean Squared Error Properties of Regression Estimators.

摘要

Mean squared error is used to compare five regression estimators: Least Squares, Principal Components, Ridge Regression, Latent Root, and a Shrunken estimator. Each of the biased estimators is shown to offer improvement in mean squared error over Least Squares for a wide range of choices of the parameters of the model. Using the results of a simulation involving all five estimators, the Principal Components and Latent Root estimators are seen to perform best overall but the Ridge Regression estimator has the potential of a smaller mean squared error than either of these providing a better estimator of the ridge parameter can be found.