Penalized least-squares estimators are discussed for estimation in inverse problem settings. Concepts of estimator efficiency involving squared error risk are examined which leads to proposals for new, data-driven, methodology for selection of the regularization parameter. A Bayesian paradigm is used to obtain "confidence" intervals that can be used with method of regularization estimators.