Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors
Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors
复制标题
DOI：
10.1016/j.jeconom.2021.05.006
复制
发表时间：
2021-06
影响因子：
6.3
通讯作者：
Dongxiao Han;Jian Huang;Yuanyuan Lin;Guohao Shen
中科院分区：
文献类型：
--
作者：
Dongxiao Han;Jian Huang;Yuanyuan Lin;Guohao Shen
针对具有截距项的高维线性模型的重尾非对称误差分布，提出了一种基于回归系数Huber损失的鲁棒后选择推理方法。在温和的条件下，得到了估计量的渐近性质。我们还扩展了所提出的方法，以适应误差项对称和其他适当条件下的异方差。对高维线性模型中低维参数或单个系数的统计检验也进行了研究。仿真研究证明了该方法具有良好的性能。提供了一个关于核黄素生产速率的基因组数据集的应用。
We propose a robust post-selection inference method based on the Huber loss for the regression coefficients, when the error distribution is heavy-tailed and asymmetric in a high-dimensional linear model with an intercept term. The asymptotic properties of the resulting estimators are established under mild conditions. We also extend the proposed method to accommodate heteroscedasticity assuming the error terms are symmetric and other suitable conditions. Statistical tests for low-dimensional parameters or individual coefficient in the high-dimensional linear model are also studied. Simulation studies demonstrate desirable properties of the proposed method. An application to a genomic dataset about riboflavin production rate is provided.