# 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} }

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

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