An analysis of the SPARSEVA estimate for the finite sample data case

Abstract

In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) estimation error in a general scheme, i.e., when the cost function is strongly convex and the regularized norm is decomposable for a pair of subspaces. We show how this general bound can be applied to a sparse regression problem to obtain an upper bound for the traditional SPARSEVA problem. Numerical results are used to illustrate the effectiveness of the suggested bound.

References in corpus (2)

• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Reweighted nuclear norm regularization: A SPARSEVA approach