  This paper develops a theory for group Lasso using a concept called strong
group sparsity. Our result shows that group Lasso is superior to standard Lasso
for strongly group-sparse signals. This provides a convincing theoretical
justification for using group sparse regularization when the underlying group
structure is consistent with the data. Moreover, the theory predicts some
limitations of the group Lasso formulation that are confirmed by simulation
studies.
