One-step sparse estimates in the reverse penalty for high-dimensional correlated data

Abstract

Variable selection methods are widely used in modeling high-dimensional data, such as portfolios, gene selection, etc. But strong correlations exist in high-dimensional data leading to poor model estimation and prediction. In this paper, inspired by Yang and Yang (2021), we propose generalized adaptive smooth adjustment for correlated effects estimator to deal with high-dimensional correlated data, reducing estimation bias and encouraging grouping, but also improving the correct rate of model identification. Furthermore, we use the one-step estimation algorithm to convert the non-convex penalty function to L1 penalty, greatly reducing the computational cost. Finally, we give some finite sample performance and case analysis of the proposed method and demonstrate the consistency of variable selection and oracle property under some conditions.