Risk bounds for model selection via penalization

It is shown that the quadratic risk of the minimum penalized empirical contrast estimator is bounded by an index of the accuracy of the sieve, which quantifies the trade-off among the candidate models between the approximation error and parameter dimension relative to sample size.