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Robust feature screening for varying coefficient models via quantile partial correlation

Author

Listed:
  • Xiang-Jie Li

    (Renmin University of China)

  • Xue-Jun Ma

    (College of Applied Sciences Beijing University of Technology)

  • Jing-Xiao Zhang

    (Renmin University of China)

Abstract

This article is concerned with feature screening for varying coefficient models with ultrahigh-dimensional predictors. We propose a new sure independence screening method based on quantile partial correlation (QPC-SIS), which is quite robust against outliers and heavy-tailed distributions. Then we establish the sure screening property for the QPC-SIS, and conduct simulations to examine its finite sample performance. The results of simulation study indicate that the QPC-SIS performs better than other methods like sure independent screening (SIS), sure independent ranking and screening, distance correlation-sure independent screening, conditional correlation sure independence screening and nonparametric independent screening, which shows the validity and rationality of QPC-SIS.

Suggested Citation

  • Xiang-Jie Li & Xue-Jun Ma & Jing-Xiao Zhang, 2017. "Robust feature screening for varying coefficient models via quantile partial correlation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 17-49, January.
    • Handle: RePEc:spr:metrik:v:80:y:2017:i:1:d:10.1007_s00184-016-0589-5
    DOI: 10.1007/s00184-016-0589-5
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    References listed on IDEAS

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