Monte Carlo Simulation for Lasso-Type Problems by Estimator Augmentation

@article{Zhou2014MonteCS,
  title={Monte Carlo Simulation for Lasso-Type Problems by Estimator Augmentation},
  author={Qing Zhou},
  journal={Journal of the American Statistical Association},
  year={2014},
  volume={109},
  pages={1495 - 1516}
}
  • Qing Zhou
  • Published 2014
  • Mathematics
  • Journal of the American Statistical Association
  • Regularized linear regression under the ℓ1 penalty, such as the Lasso, has been shown to be effective in variable selection and sparse modeling. The sampling distribution of an ℓ1-penalized estimator is hard to determine as the estimator is defined by an optimization problem that in general can only be solved numerically and many of its components may be exactly zero. Let S be the subgradient of the ℓ1 norm of the coefficient vector β evaluated at . We find that the joint sampling distribution… CONTINUE READING

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 14 CITATIONS

    Constructing confidence sets after lasso selection by randomized estimator augmentation

    VIEW 12 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Estimator Augmentation with Applications in High-Dimensional Group Inference

    VIEW 8 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    Uncertainty quantification under group sparsity

    VIEW 8 EXCERPTS
    CITES BACKGROUND, METHODS & RESULTS
    HIGHLY INFLUENCED

    References

    Publications referenced by this paper.