Electronic Journal of Statistics

Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators

Karim Lounici

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We derive the l convergence rate simultaneously for Lasso and Dantzig estimators in a high-dimensional linear regression model under a mutual coherence assumption on the Gram matrix of the design and two different assumptions on the noise: Gaussian noise and general noise with finite variance. Then we prove that simultaneously the thresholded Lasso and Dantzig estimators with a proper choice of the threshold enjoy a sign concentration property provided that the non-zero components of the target vector are not too small.

Article information

Electron. J. Statist., Volume 2 (2008), 90-102.

First available in Project Euclid: 12 February 2008

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62J05: Linear regression
Secondary: 62F12: Asymptotic properties of estimators

Linear model Lasso Dantzig Sparsity Model selection Sign consistency


Lounici, Karim. Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators. Electron. J. Statist. 2 (2008), 90--102. doi:10.1214/08-EJS177. https://projecteuclid.org/euclid.ejs/1202844625

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