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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