A Unifying Framework of High-Dimensional Sparse Estimation with Difference-of-Convex (DC) Regularizations

Abstract: Under the linear regression framework, we study the variable selection problem when the underlying model is assumed to have a small number of nonzero coefficients (i.e., the underlying linear model is sparse). Non-convex penalties in specific forms are well-studied in the literature for sparse estimation. A recent work \cite{ahn2016difference} has pointed out that nearly all existing non-convex penalties can be represented as difference-of-convex (DC) functions, which can be expressed as the difference of two convex functions, while itself may not be convex. There is a large existing literature on the optimization problems when their objectives and/or constraints involve DC functions. Efficient numerical solutions have been proposed. Under the DC framework, directional-stationary (d-stationary) solutions are considered, and they are usually not unique. In this paper, we show that under some mild conditions, a certain subset of d-stationary solutions in an optimization problem (with a DC objective) has some ideal statistical properties: namely, asymptotic estimation consistency, asymptotic model selection consistency, asymptotic efficiency. The aforementioned properties are the ones that have been proven by many researchers for a range of proposed non-convex penalties in the sparse estimation. Our assumptions are either weaker than or comparable with those conditions that have been adopted in other existing works. This work shows that DC is a nice framework to offer a unified approach to these existing work where non-convex penalty is involved. Our work bridges the communities of optimization and statistics.