Global optimality of nonconvex penalized estimators
非凸惩罚估计量的全局最优性
Nonconvex penalties such as the smoothly clipped absolute deviation or minimax concave penalties have desirable properties such as the oracle property, even when the dimension of the predictive variables is large. However, checking whether a given local minimizer has such properties is not easy since there can be many local minimizers. In this paper, we give sufficient conditions under which a local minimizer is unique, and show that the oracle estimator becomes the unique…