Feature screening under missing indicator imputation with non-ignorable missing response
Feature screening under missing indicator imputation with non-ignorable missing response
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DOI：
10.1016/j.csda.2020.106975
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Jing Zhang;Qihua Wang;Jian Kang
中科院分区：
文献类型：
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作者：
Jing Zhang;Qihua Wang;Jian Kang
本文开发了一种在超高维数据分析中具有不可忽略的缺失响应的无模型变量筛选技术。基于倾向函数的通用逻辑模型假设，通过借用缺失指标的隐藏信息，提出了一种新的筛选程序，使得任何具有全数据的超高维协变量的变量筛选方法都可以应用于不可忽略的缺失响应情况。结果表明，只要相应的全数据筛选方法具有确定筛选性，就可以保持确定筛选性。通过功能神经影像数据的一些模拟和分析证明了所提出方法的有限样本性能。
This article develops a model-free variable screening technique with the non-ignorable missing response in ultrahigh-dimensional data analysis. Based on the common logistic model assumption of the propensity function, a novel screening procedure is proposed by borrowing hidden information of missingness indicator such that any variable screening method for ultrahigh-dimensional covariates with full data can be applied to the non-ignorable missing response case. And it is shown that the sure screening property can be kept as long as the corresponding screening method for full data is of sure screening property. The finite sample performances of the proposed method are demonstrated via some simulations and analysis of functional neuroimaging data.