Feature screening in ultrahigh-dimensional partially linear models with missing responses at random
Feature screening in ultrahigh-dimensional partially linear models with missing responses at random
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DOI：
10.1016/j.csda.2018.10.003
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Niansheng Tang;Linli Xia;Xiaodong Yan
中科院分区：
文献类型：
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作者：
Niansheng Tang;Linli Xia;Xiaodong Yan
针对纵向数据随机缺失响应的超高维部分线性模型，提出了一种新的基于轮廓边缘核辅助估计方程插补技术的特征筛选方法.所提出的特征筛选程序有三个主要优点。首先，它是计算效率高，并可用于筛选显着的协变量，在存在缺失的响应。其次，它不需要估计应答概率，并且对应答概率模型的错误指定具有鲁棒性。第三，采用单变量核平滑方法估计非参数函数，并用于随机插补估计方程的缺失响应，避免了众所周知的“维数灾难”。在一定的正则性条件下，证明了该算法的排序一致性和确定性筛选性。仿真研究进行调查有限样本性能的建议筛选程序。一个例子是用来说明所提出的程序。
This paper proposes a new feature screening procedure in ultrahigh-dimensional partially linear models with missing responses at random for longitudinal data based on the profile marginal kernel-assisted estimating equations imputation technique. The proposed feature screening procedure has three key merits. First, it is computationally efficient, and can be used to screen significant covariates in the presence of missing responses. Second, it does not require estimating respondent probability and is robust to the misspecification of respondent probability models. Third, the univariate kernel smoothing method is adopted to estimate nonparametric functions, and is employed to impute estimating equations with missing responses at random, which avoids the well-known “curse of dimensionality”. The ranking consistency property and the sure screening property are shown under some regularity conditions. Simulation studies are conducted to investigate the finite sample performance of the proposed screening procedure. An example is used to illustrate the proposed procedure.