Exponential Screening and optimal rates of sparse estimation

TL;DR

This paper introduces Exponential Screening, a new estimator for high-dimensional sparse regression that adaptively balances mean squared error and sparsity, achieving optimal rates and outperforming existing methods.

Contribution

The paper proposes Exponential Screening, a novel adaptive estimation procedure that optimally combines sparsity and accuracy in high-dimensional regression, with proven minimax optimality.

Findings

Exponential Screening adapts to the best sparsity-accuracy trade-off.

It achieves optimal rates in Gaussian regression aggregation problems.

It outperforms state-of-the-art sparse estimation methods.

Abstract

In high-dimensional linear regression, the goal pursued here is to estimate an unknown regression function using linear combinations of a suitable set of covariates. One of the key assumptions for the success of any statistical procedure in this setup is to assume that the linear combination is sparse in some sense, for example, that it involves only few covariates. We consider a general, non necessarily linear, regression with Gaussian noise and study a related question that is to find a linear combination of approximating functions, which is at the same time sparse and has small mean squared error (MSE). We introduce a new estimation procedure, called Exponential Screening that shows remarkable adaptation properties. It adapts to the linear combination that optimally balances MSE and sparsity, whether the latter is measured in terms of the number of non-zero entries in the combination ($\ell_0$ norm) or in terms of the global weight of the combination ($\ell_1$ norm). The power of this adaptation result is illustrated by showing that Exponential Screening solves optimally and simultaneously all the problems of aggregation in Gaussian regression that have been discussed in the literature. Moreover, we show that the performance of the Exponential Screening estimator cannot be improved in a minimax sense, even if the optimal sparsity is known in advance. The theoretical and numerical superiority of Exponential Screening compared to state-of-the-art sparse procedures is also discussed.