Monte Carlo Simulation for Lasso-Type Problems by Estimator Augmentation

17 Jan 2014 Qing Zhou

Regularized linear regression under the $\ell_1$ penalty, such as the Lasso, has been shown to be effective in variable selection and sparse modeling. The sampling distribution of an $\ell_1$-penalized estimator $\hat{\beta}$ 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... (read more)

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