Unveiling low-dimensional patterns induced by convex non-differentiable regularizers
This paper explores the asymptotic distributions of low-dimensional patterns in linear regression with regularizers such as Lasso, Elastic Net, Generalized Lasso, and SLOPE, as the number of observations n grows and the penalty increases at rate n. While the asymptotic distribution of rescaled estimation errors is well-understood, convergence of patterns lacks proof in the literature, even for Las
