Code for: The Proximal Bootstrap for Finite-Dimensional Regularized Estimators

We propose a proximal bootstrap that can consistently estimate the limiting distribution of $\sqrt{n}$ consistent estimators with nonstandard asymptotic distributions in a computationally efficient manner by formulating the proximal bootstrap estimator as the solution to a convex optimization problem, which can have a closed form solution for certain designs. This paper considers the application to finite-dimensional regularized estimators, such as the Lasso, $\ell_{1}$ norm regularized quantile regression, $\ell_{1}$ norm support vector regression, and trace regression via nuclear norm regularization.