Admissible variable-selection procedures when fitting regression models by least squares for prediction

Abstract

Given data with a single dependent variable arising from a normal linear regression model, a variable-selection procedure completely specifies a least squares fit by choosing the subset of the independent variables to include in the model. For loss equal to squared error of prediction, we prove that all variable-selection procedures are admissible for choosing among least-squares fits of the regression model.