Aggregation for Linear Inverse Problems

In the framework of inverse problems, we consider the question of aggregating estimators taken from a given collection. Extending usual results for the direct case, we propose a new penalty to achieve the best aggregation. An oracle inequality provides the asymptotic behavior of this estimator. We investigate here the price for considering indirect observations.