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Boosting for high-dimensional linear models. (English) Zbl 1095.62077
Summary: We prove that boosting with the squared error loss, $L_2$-Boosting, 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 $\ell_1$-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 $\ell_1$-norm. We also propose here an AIC-based method for tuning, namely for choosing the number of boosting iterations. This makes $L_2$-Boosting computationally attractive since it is not required to run the algorithm multiple times for cross-validation as commonly used so far. We demonstrate $L_2$-Boosting 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.

62J05Linear regression
62B10Statistical information theory
65C60Computational problems in statistics
49M15Newton-type methods in calculus of variations
62P10Applications of statistics to biology and medical sciences
68Q32Computational learning theory
62J99Linear statistical inference
cran; PDCO
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