Electronic Journal of Statistics

Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators

Yuchen Zhang, Martin J. Wainwright, and Michael I. Jordan

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For the problem of high-dimensional sparse linear regression, it is known that an $\ell_{0}$-based estimator can achieve a $1/n$ “fast” rate for prediction error without any conditions on the design matrix, whereas in the absence of restrictive conditions on the design matrix, popular polynomial-time methods only guarantee the $1/\sqrt{n}$ “slow” rate. In this paper, we show that the slow rate is intrinsic to a broad class of M-estimators. In particular, for estimators based on minimizing a least-squares cost function together with a (possibly nonconvex) coordinate-wise separable regularizer, there is always a “bad” local optimum such that the associated prediction error is lower bounded by a constant multiple of $1/\sqrt{n}$. For convex regularizers, this lower bound applies to all global optima. The theory is applicable to many popular estimators, including convex $\ell_{1}$-based methods as well as M-estimators based on nonconvex regularizers, including the SCAD penalty or the MCP regularizer. In addition, we show that bad local optima are very common, in that a broad class of local minimization algorithms with random initialization typically converge to a bad solution.

Article information

Electron. J. Statist., Volume 11, Number 1 (2017), 752-799.

Received: November 2015
First available in Project Euclid: 11 March 2017

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

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

Sparse linear regression high-dimensional statistics computationally-constrained minimax theory nonconvex optimization

Creative Commons Attribution 4.0 International License.


Zhang, Yuchen; Wainwright, Martin J.; Jordan, Michael I. Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators. Electron. J. Statist. 11 (2017), no. 1, 752--799. doi:10.1214/17-EJS1233. https://projecteuclid.org/euclid.ejs/1489201320

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