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The Proximal Bootstrap for Finite-Dimensional Regularized Estimators

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  • Jessie Li

Abstract

We propose a proximal bootstrap that can consistently estimate the limiting distribution of √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, ℓ1-norm regularized quantile regression, ℓ1-norm support vector regression, and trace regression via nuclear norm regularization.

Suggested Citation

  • Jessie Li, 2021. "The Proximal Bootstrap for Finite-Dimensional Regularized Estimators," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 616-620, May.
    • Handle: RePEc:aea:apandp:v:111:y:2021:p:616-20
    DOI: 10.1257/pandp.20211036
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    Cited by:

    1. Jean-Jacques Forneron, 2022. "Estimation and Inference by Stochastic Optimization," Papers 2205.03254, arXiv.org.
    2. Forneron, Jean-Jacques, 2024. "Estimation and inference by stochastic optimization," Journal of Econometrics, Elsevier, vol. 238(2).

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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