Oracle properties of the adaptive elastic net

This paper proposes an adaptive elastic net for sparse high-dimension regression by incorporating the adaptive penalty mechanism into the elastic net. Under a partial orthogonality condition, the initial univariate regression estimator is shown to be zero-consistent, based on which the consistent variable selection and the asymptotic estimation normality is obtained. In addition, the controllable group selection capability is also achieved by properly selecting a special rescaling factor.