-Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model. Issue 2 (3rd April 2023)

• Record Type: Journal Article
• Authors: Pan, Zhewen; Xie, Jianhui
• Is Part Of: Journal of business & economic statistics. Volume 41:Issue 2(2023)
• Page Start: 283
• Page End: 297
• Publication Date: 2023-04-03
• Subjects: Alternating direction method of multipliers -- LASSO -- Post-LASSO -- Tobit model -- U-process; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2021.2013243
• Languages: English
• ISSNs: 0735-0015

Abstract

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 l 1 -penalization method with the ideas of pairwise difference and propose an l 1 -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 l 1 -penalized estimator, and find that the post-penalized estimator generally can perform better than the l 1 -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.