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Feature screening under missing indicator imputation with non-ignorable missing response

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Listed:
  • Zhang, Jing
  • Wang, Qihua
  • Kang, Jian

Abstract

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.

Suggested Citation

  • Zhang, Jing & Wang, Qihua & Kang, Jian, 2020. "Feature screening under missing indicator imputation with non-ignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:csdana:v:149:y:2020:i:c:s0167947320300669
    DOI: 10.1016/j.csda.2020.106975
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    References listed on IDEAS

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    1. Qihua Wang & Yongjin Li, 2018. "How to Make Model†free Feature Screening Approaches for Full Data Applicable to the Case of Missing Response?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(2), pages 324-346, June.
    2. repec:mpr:mprres:8160 is not listed on IDEAS
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