In the general statistical experiment model we propose a procedure for selecting an estimator from a given family of linear estimators. We derive an upper bound on the risk of the selected estimator and demontrate how this result can be used in order to construct minimax and adaptive minimax estimators in specic nonparametric estimation problems.