• DOI:10.1214/07-AOS582
Corpus ID: 9212916

LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA

@article{Meinshausen2009LASSOTYPERO,
  title={LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA},
  author={N. Meinshausen and B. Yu},
  journal={Annals of Statistics},
  year={2009},
  volume={37},
  pages={246-270}
}

N. Meinshausen, B. Yu

Published 2009

Mathematics

Annals of Statistics

The Lasso [28] is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables p is potentially much larger than the number of samples n. However, it was recently discovered [23, 38, 39] that the sparsity pattern of the Lasso estimator can only be asymptotically identical to the true sparsity pattern if the design matrix satisfies the so-called irrepresentable condition. The latter condition can easily be violated in… CONTINUE READING

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