Electronic Journal of Statistics

LASSO, Iterative Feature Selection and the Correlation Selector: Oracle inequalities and numerical performances

Pierre Alquier

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We propose a general family of algorithms for regression estimation with quadratic loss, on the basis of geometrical considerations. These algorithms are able to select relevant functions into a large dictionary. We prove that a lot of methods that have already been studied for this task (LASSO, Dantzig selector, Iterative Feature Selection, among others) belong to our family, and exhibit another particular member of this family that we call Correlation Selector in this paper. Using general properties of our family of algorithm we prove oracle inequalities for IFS, for the LASSO and for the Correlation Selector, and compare numerical performances of these estimators on a toy example.

Article information

Electron. J. Statist., Volume 2 (2008), 1129-1152.

First available in Project Euclid: 21 November 2008

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression
Secondary: 62J07: Ridge regression; shrinkage estimators 62G15: Tolerance and confidence regions 68T05: Learning and adaptive systems [See also 68Q32, 91E40]

Regression estimation statistical learning confidence regions shrinkage and thresholding methods LASSO


Alquier, Pierre. LASSO, Iterative Feature Selection and the Correlation Selector: Oracle inequalities and numerical performances. Electron. J. Statist. 2 (2008), 1129--1152. doi:10.1214/08-EJS288. https://projecteuclid.org/euclid.ejs/1227287695

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