Square-root lasso: Pivotal recovery of sparse signals via conic programming

Published

Journal Article

We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are significant. The method is a modification of the lasso, called the square-root lasso. The method is pivotal in that it neither relies on the knowledge of the standard deviation σ nor does it need to pre-estimate σ. Moreover, the method does not rely on normality or sub-Gaussianity of noise. It achieves near-oracle performance, attaining the convergence rate σ(s/n) log p 1/2 in the prediction norm, and thus matching the performance of the lasso with known σ. These performance results are valid for both Gaussian and non-Gaussian errors, under some mild moment restrictions. We formulate the square-root lasso as a solution to a convex conic programming problem, which allows us to implement the estimator using efficient algorithmic methods, such as interior-point and first-order methods. © 2011 Biometrika Trust.

Full Text

Duke Authors

Cited Authors

  • Belloni, A; Chernozhukov, V; Wang, L

Published Date

  • December 1, 2011

Published In

Volume / Issue

  • 98 / 4

Start / End Page

  • 791 - 806

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asr043

Citation Source

  • Scopus