Strong universal consistent estimate of the minimum mean squared error

Abstract

Consider the regression problem with a response variable Y and a feature vector X. For the regression function m(x)= E{Y ∣ X=x}, we introduce new and simple estimators of the minimum mean squared error L*=E{(Y — m(X))2}, and prove their strong consistencies. We bound the rate of convergence, too.