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 of the estimation error for the traditional I-1 SPARSEVA problem. Numerical results are used to illustrate the effectiveness of the suggested bound. (C) 2018 Elsevier Ltd. All rights reserved.