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

Principal Investigator(s):  View help for Principal Investigator(s) Jessie Li, UC Santa Cruz

Version:  View help for Version V1

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:  View help for 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:  View help for Subject Terms bootstrap; convex optimization ; proximal mapping
JEL Classification:  View help for JEL Classification
      C15 Statistical Simulation Methods: General
      C51 Model Construction and Estimation


Related Publications

Download this project

Published Versions

V1 [2021-05-17]

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as received from the data depositor. As of April 2026, depositors are required to submit study materials in accessible formats. ICPSR has not reviewed, checked, or processed this material. For additional information about the study, please contact the investigator(s) directly. If you have questions about the accessibility of materials distributed by ICPSR or require further assistance, please visit ICPSR's Accessibility Center.