Open Access
November 2012 High-Dimensional Regression with Unknown Variance
Christophe Giraud, Sylvie Huet, Nicolas Verzelen
Statist. Sci. 27(4): 500-518 (November 2012). DOI: 10.1214/12-STS398


We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasis is put on nonasymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the lasso estimator and some references are collected for some more general models, including multivariate regression and nonparametric regression.


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Christophe Giraud. Sylvie Huet. Nicolas Verzelen. "High-Dimensional Regression with Unknown Variance." Statist. Sci. 27 (4) 500 - 518, November 2012.


Published: November 2012
First available in Project Euclid: 21 December 2012

zbMATH: 1331.62346
MathSciNet: MR3025131
Digital Object Identifier: 10.1214/12-STS398

Keywords: high-dimension , Linear regression , unknown variance

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.27 • No. 4 • November 2012
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