Sparsity identification in ultra-high dimensional quantile regression models with longitudinal data
Sparsity identification in ultra-high dimensional quantile regression models with longitudinal data
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DOI：
10.1080/03610926.2019.1604966
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发表时间：
2019-04-24
影响因子：
0.8
通讯作者：
Liu, Qiang
中科院分区：
文献类型：
--
作者：
Gao, Xianli;Liu, Qiang
本文提出了一种双鲁棒加权自适应鲁棒套索（WAR-Lasso）方法，用于超高维纵向数据的分位数回归模型的变量选择。我们推导了WAR-Lasso的一致性和模型选择oracle属性。仿真研究表明，在误差的重尾分布和协变量的重污染情况下，WAR-Lasso具有双鲁棒性。WAR-Lasso优于SCAD等其他方法。并对实际数据进行了分析。这表明WAR-Lasso倾向于选择较少的变量，估计的系数符合经济显著性。
In this paper, we propose a variable selection method for quantile regression model in ultra-high dimensional longitudinal data called as the weighted adaptive robust lasso (WAR-Lasso) which is double-robustness. We derive the consistency and the model selection oracle property of WAR-Lasso. Simulation studies show the double-robustness of WAR-Lasso in both cases of heavy-tailed distribution of the errors and the heavy contaminations of the covariates. WAR-Lasso outperform other methods such as SCAD and etc. A real data analysis is carried out. It shows that WAR-Lasso tends to select fewer variables and the estimated coefficients are in line with economic significance.