-Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model

Zhewen Pan and Jianhui Xie

Journal of Business & Economic Statistics, 2023, vol. 41, issue 2, 283-297

Abstract: High-dimensional data are nowadays readily available and increasingly common in various fields of empirical economics. This article considers estimation and model selection for a high-dimensional censored linear regression model. We combine l1 -penalization method with the ideas of pairwise difference and propose an l1 -penalized pairwise difference least absolute deviations (LAD) estimator. Estimation consistency and model selection consistency of the estimator are established under regularity conditions. We also propose a post-penalized estimator that applies unpenalized pairwise difference LAD estimation to the model selected by the l1 -penalized estimator, and find that the post-penalized estimator generally can perform better than the l1 -penalized estimator in terms of the rate of convergence. Novel fast algorithms for computing the proposed estimators are provided based on the alternating direction method of multipliers. A simulation study is conducted to show the great improvements of our algorithms in terms of computation time and to illustrate the satisfactory statistical performance of our estimators.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:41:y:2023:i:2:p:283-297

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DOI: 10.1080/07350015.2021.2013243

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