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

@inproceedings{Meinshausen2006LASSOTYPERO,
  title={LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA By},
  author={Nicolai Meinshausen and Bin Yu},
  year={2006}
}
The Lasso (Tibshirani, 1996) 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 (Zhao and Yu, 2006; Zou, 2005; Meinshausen and Bühlmann, 2006) 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… CONTINUE READING
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