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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.

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Autor(es)

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

Id.: 69702381

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveSparse linear regression - 

Tipo de recurso: Text  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: Copyright 2017 The Institute of Mathematical Statistics and the Bernoulli Society

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] 1935-7524

Fecha de contribución: 26-abr-2017

Contacto:

Localización:
* Electron. J. Statist. 11, no. 1 (2017), 752-799
* doi:10.1214/17-EJS1233

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