A class of shrinkage estimators in linear regression

We consider the problem of using shrinkage estimators that shrink towards subspaces in linear regression, in particular subspaces spanned by principal components. This is especially important when multicollinearity is present and the number of predictors is not small compared to the sample size. New theoretical results about Stein estimation are used to get estimators with lower theoretical risk than standard Stein estimators used by Oman (1991). Application of the techniques to real data is largely successful.