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
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application/pdf | 32.5 KB | 01/19/2021 11:31:AM |
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text/x-matlab | 3.2 KB | 01/19/2021 11:28:AM |
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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:
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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:
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bootstrap;
convex optimization ;
proximal mapping
JEL Classification:
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C15 Statistical Simulation Methods: General
C51 Model Construction and Estimation
C15 Statistical Simulation Methods: General
C51 Model Construction and Estimation
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