论文标题
可变选择的强大方法，具有高维逻辑数据分析
Robust approach for variable selection with high dimensional Logitudinal data analysis
论文作者
论文摘要
本文提出了一个新的可靠的平滑阈值估计方程，以选择重要变量，并自动估计高维纵向数据的参数。提出了一种新型的工作相关矩阵来捕获同一主题中的相关性。当协变量p的数量随着受试者n的增加而增加时，提出的程序效果很好。所提出的估计值具有具有真实相关结构获得的估计值，尤其是当数据污染时。此外，提出的方法对响应变量和/或协变量中的异常值具有鲁棒性。此外，在某些规律性条件下建立了“大N，Diverging P”下可靠的平滑阈值估计方程的甲骨文属性。广泛的仿真研究和酵母细胞周期数据用于评估所提出的方法的性能，结果表明，我们所提出的方法具有现有的鲁棒变量选择程序具有竞争力。
This paper proposes a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates p increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth-threshold estimating equations under "large n, diverging p" are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that our proposed method is competitive with existing robust variable selection procedures.
