估计员
成对比较
数学
一致性(知识库)
选型
收敛速度
最小绝对偏差
统计
计算
数学优化
应用数学
算法
计算机科学
钥匙(锁)
几何学
计算机安全
作者
Zhewen Pan,Jianhui Xie
标识
DOI:10.1080/07350015.2021.2013243
摘要
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.
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