Adaptive nonparametric estimation by selection of estimators

The talk is about methods of adaptive nonparametric estimation based on selection of an estimator from a fixed family of linear estimators. We propose a general selection procedure and show that this procedure leads to adaptive minimax estimators in a wide variety of estimation settings. In particular, the resulting estimators can adapt both to unknown smoothness and structure of the function to be estimated.