The Annals of Statistics

On the exponentially weighted aggregate with the Laplace prior

Arnak S. Dalalyan, Edwin Grappin, and Quentin Paris

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In this paper, we study the statistical behaviour of the Exponentially Weighted Aggregate (EWA) in the problem of high-dimensional regression with fixed design. Under the assumption that the underlying regression vector is sparse, it is reasonable to use the Laplace distribution as a prior. The resulting estimator and, specifically, a particular instance of it referred to as the Bayesian lasso, was already used in the statistical literature because of its computational convenience, even though no thorough mathematical analysis of its statistical properties was carried out. The present work fills this gap by establishing sharp oracle inequalities for the EWA with the Laplace prior. These inequalities show that if the temperature parameter is small, the EWA with the Laplace prior satisfies the same type of oracle inequality as the lasso estimator does, as long as the quality of estimation is measured by the prediction loss. Extensions of the proposed methodology to the problem of prediction with low-rank matrices are considered.

Article information

Ann. Statist., Volume 46, Number 5 (2018), 2452-2478.

Received: December 2016
Revised: July 2017
First available in Project Euclid: 17 August 2018

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62J05: Linear regression
Secondary: 62H12: Estimation

Sparsity Bayesian lasso oracle inequality exponential weights high-dimensional regression trace regression low-rank matrices


Dalalyan, Arnak S.; Grappin, Edwin; Paris, Quentin. On the exponentially weighted aggregate with the Laplace prior. Ann. Statist. 46 (2018), no. 5, 2452--2478. doi:10.1214/17-AOS1626.

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Supplemental materials

  • Supplement to “On the exponentially weighted aggregate with the Laplace prior”. The proofs of equation (10), as well as the proofs of results of Section 5, have been gathered in the Supplementary Material.