On the oracle property of a generalized adaptive elastic-net for multivariate linear regression with a diverging number of parameters

Abstract

In this paper, we examine the problem of variable selection and coefficient estimation in multivariate linear regression with a diverging number of parameters. We propose a generalized adaptive elastic-net method that integrates elastic-net regularization, adaptively weighted penalty, and covariance of errors. Under some weak regularity conditions, we establish the oracle property of generalized adaptive elastic-net estimators. We also present an algorithm based on the subgradient method to solve the generalized adaptive elastic-net estimation. The numerical computation results show that the proposed method has variable selection accuracy that is comparable to those of the existing methods but that it also has more correct-fitting, less over-fitting, and is closest to the oracle estimator in terms of mean squares error. Moreover, we analyze a chemometrics data set to illustrate our methodology.