A Tuning-free Robust and Efficient Approach to High-dimensional Regression

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.


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Work Title A Tuning-free Robust and Efficient Approach to High-dimensional Regression
Open Access
  1. Lan Wang
  2. Bo Peng
  3. Jelena Bradic
  4. Runze Li
  5. Yunan Wu
License In Copyright (Rights Reserved)
Work Type Article
  1. Journal of the American Statistical Association
Publication Date January 1, 2020
Publisher Identifier (DOI)
  1. https://doi.org/10.1080/01621459.2020.1840989
Deposited July 19, 2022




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Work History

Version 1

  • Created
  • Added 01621459.2020-3.pdf
  • Added Creator Lan Wang
  • Added Creator Bo Peng
  • Added Creator Jelena Bradic
  • Added Creator Runze Li
  • Added Creator Yunan Wu
  • Published