A Tuning-free Robust and Efficient Approach to High-dimensional Regression. Issue 532 (11th December 2020)

• Record Type: Journal Article
• Authors: Wang, Lan; Peng, Bo; Bradic, Jelena; Li, Runze; Wu, Yunan
• Is Part Of: Journal of the American Statistical Association. Volume 115:Issue 532(2020)
• Page Start: 1700
• Page End: 1714
• Publication Date: 2020-12-11
• Subjects: Efficiency -- Heavy-tailed error -- High dimension -- Linear regression -- Tuning parameter -- Robustness; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2020.1840989
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract: We introduce a novel approach for high-dimensional regression with theoretical guarantees. The new procedure overcomes the challenge of tuning parameter selection of Lasso and possesses several appealing properties. It uses an easily simulated tuning parameter that automatically adapts to both the unknown random error distribution and the correlation structure of the design matrix. It is robust with substantial efficiency gain for heavy-tailed random errors while maintaining high efficiency for normal random errors. Comparing with other alternative robust regression procedures, it also enjoys the property of being equivariant when the response variable undergoes a scale transformation. Computationally, it can be efficiently solved via linear programming. Theoretically, under weak conditions on the random error distribution, we establish a finite-sample error bound with a near-oracle rate for the new estimator with the simulated tuning parameter. Our results make useful contributions to mending the gap between the practice and theory of Lasso and its variants. We also prove that further improvement in efficiency can be achieved by a second-stage enhancement with some light tuning. Our simulation results demonstrate that the proposed methods often outperform cross-validated Lasso in various settings.