## The Annals of Statistics

### Boosting for high-dimensional linear models

Peter Bühlmann

#### Abstract

We prove that boosting with the squared error loss, L2Boosting, is consistent for very high-dimensional linear models, where the number of predictor variables is allowed to grow essentially as fast as O(exp(sample size)), assuming that the true underlying regression function is sparse in terms of the 1-norm of the regression coefficients. In the language of signal processing, this means consistency for de-noising using a strongly overcomplete dictionary if the underlying signal is sparse in terms of the 1-norm. We also propose here an AIC-based method for tuning, namely for choosing the number of boosting iterations. This makes L2Boosting computationally attractive since it is not required to run the algorithm multiple times for cross-validation as commonly used so far. We demonstrate L2Boosting for simulated data, in particular where the predictor dimension is large in comparison to sample size, and for a difficult tumor-classification problem with gene expression microarray data.

#### Article information

Source
Ann. Statist., Volume 34, Number 2 (2006), 559-583.

Dates
First available in Project Euclid: 27 June 2006

Permanent link to this document
https://projecteuclid.org/euclid.aos/1151418234

Digital Object Identifier
doi:10.1214/009053606000000092

Mathematical Reviews number (MathSciNet)
MR2281878

Zentralblatt MATH identifier
1095.62077

#### Citation

Bühlmann, Peter. Boosting for high-dimensional linear models. Ann. Statist. 34 (2006), no. 2, 559--583. doi:10.1214/009053606000000092. https://projecteuclid.org/euclid.aos/1151418234

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