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

Name	File Type	Size	Last Modified
README.pdf	application/pdf	32.5 KB	01/19/2021 11:31:AM
lasso.m	text/x-matlab	3.2 KB	01/19/2021 11:28:AM
lassostandardboot.m	text/x-matlab	2.6 KB	01/18/2021 12:52:PM

Project Citation:

Li, Jessie. Code for: The Proximal Bootstrap for Finite-Dimensional Regularized Estimators. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-05-17. https://doi.org/10.3886/E130627V1

Project Description

Summary: 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.

Scope of Project

Subject Terms: bootstrap; convex optimization ; proximal mapping

JEL Classification:
C15 Statistical Simulation Methods: General
C51 Model Construction and Estimation

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Published Versions

V1 [2021-05-17]

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