Optimistic lower bounds for convex regularized least-squares

Abstract: Minimax lower bounds are pessimistic in nature: for any given estimator, minimax lower bounds yield the existence of a worst-case target vector $\beta^*_{worst}$ for which the prediction error of the given estimator is bounded from below. However, minimax lower bounds shed no light on the prediction error of the given estimator for target vectors different than $\beta^*_{worst}$. A characterization of the prediction error of any convex regularized least-squares is given. This characterization provide both a lower bound and an upper bound on the prediction error. This produces lower bounds that are applicable for any target vector and not only for a single, worst-case $\beta^*_{worst}$. Finally, these lower and upper bounds on the prediction error are applied to the Lasso is sparse linear regression. We obtain a lower bound involving the compatibility constant for any tuning parameter, matching upper and lower bounds for the universal choice of the tuning parameter, and a lower bound for the Lasso with small tuning parameter.