L1-penalized pairwise difference estimation for a high-dimensional censored regression model
L1-penalized pairwise difference estimation for a high-dimensional censored regression model
复制标题
高维删失回归模型的 L1 惩罚成对差异估计
DOI：
10.1080/07350015.2021.2013243
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发表时间：
2022
影响因子：
3
通讯作者：
Xie Jianhui
中科院分区：
文献类型：
--
作者：
Pan Zhewen;Xie Jianhui
高维数据现在很容易获得，并且在经验经济学的各个领域越来越普遍。本文研究了高维删失线性回归模型的估计和模型选择问题。将惩罚方法与两两差分的思想相结合，提出了无惩罚两两差分最小绝对偏差（LAD）估计。在正则性条件下，证明了估计量的估计相合性和模型选择相合性。我们还提出了一个后惩罚估计，适用于未惩罚的成对差分LAD估计所选择的模型的惩罚估计，并发现后惩罚估计一般可以执行比惩罚估计的收敛速度。基于乘子交替方向法，提出了新的快速算法。仿真研究表明，我们的算法在计算时间方面有很大的改进，并说明我们的估计令人满意的统计性能。
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-penalization method with the ideas of pairwise difference and propose an-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-penalized estimator, and find that the post-penalized estimator generally can perform better than the-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.