The Annals of Statistics

Slope meets Lasso: Improved oracle bounds and optimality

Pierre C. Bellec, Guillaume Lecué, and Alexandre B. Tsybakov

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We show that two polynomial time methods, a Lasso estimator with adaptively chosen tuning parameter and a Slope estimator, adaptively achieve the minimax prediction and $\ell_{2}$ estimation rate $(s/n)\log(p/s)$ in high-dimensional linear regression on the class of $s$-sparse vectors in $\mathbb{R}^{p}$. This is done under the Restricted Eigenvalue (RE) condition for the Lasso and under a slightly more constraining assumption on the design for the Slope. The main results have the form of sharp oracle inequalities accounting for the model misspecification error. The minimax optimal bounds are also obtained for the $\ell_{q}$ estimation errors with $1\le q\le2$ when the model is well specified. The results are nonasymptotic, and hold both in probability and in expectation. The assumptions that we impose on the design are satisfied with high probability for a large class of random matrices with independent and possibly anisotropically distributed rows. We give a comparative analysis of conditions, under which oracle bounds for the Lasso and Slope estimators can be obtained. In particular, we show that several known conditions, such as the RE condition and the sparse eigenvalue condition are equivalent if the $\ell_{2}$-norms of regressors are uniformly bounded.

Article information

Ann. Statist., Volume 46, Number 6B (2018), 3603-3642.

Received: May 2016
Revised: May 2017
First available in Project Euclid: 11 September 2018

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Zentralblatt MATH identifier

Primary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43] 62G08: Nonparametric regression
Secondary: 62C20: Minimax procedures 62G05: Estimation 62G20: Asymptotic properties

Sparse linear regression minimax rates high-dimensional statistics Slope Lasso


Bellec, Pierre C.; Lecué, Guillaume; Tsybakov, Alexandre B. Slope meets Lasso: Improved oracle bounds and optimality. Ann. Statist. 46 (2018), no. 6B, 3603--3642. doi:10.1214/17-AOS1670.

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