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A note on quantile feature screening via distance correlation

Author

Listed:
  • Xiaolin Chen

    (Qufu Normal University)

  • Xiaojing Chen

    (Qufu Normal University)

  • Yi Liu

    (China University of Petroleum)

Abstract

In this paper, we propose a new feature screening procedure based on a robust quantile version of distance correlation with some desirable characters. First, it is particularly useful for data exhibiting heterogeneity, which is very common for high dimensional data. Second, it is robust to model misspecification and behaves reliably when some of features contain outliers or follow heavy-tailed distributions. Under very mild conditions, we have established its sure screening property. In practice, a same index set is often found to be adequate by the quantile analysis. So we furthermore present a composite robust quantile version of distance correlation to perform feature screening. Simulation studies are carried out to examine the performance of advised procedures. We also illustrate them by a real data example.

Suggested Citation

  • Xiaolin Chen & Xiaojing Chen & Yi Liu, 2019. "A note on quantile feature screening via distance correlation," Statistical Papers, Springer, vol. 60(5), pages 1741-1762, October.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:5:d:10.1007_s00362-017-0894-8
    DOI: 10.1007/s00362-017-0894-8
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    References listed on IDEAS

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    Cited by:

    1. Kangning Wang & Mengjie Hao & Xiaofei Sun, 2021. "Robust and efficient estimating equations for longitudinal data partial linear models and its applications," Statistical Papers, Springer, vol. 62(5), pages 2147-2168, October.
    2. Lu, Shuiyun & Chen, Xiaolin & Xu, Sheng & Liu, Chunling, 2020. "Joint model-free feature screening for ultra-high dimensional semi-competing risks data," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    3. Jing Zhang & Yanyan Liu & Hengjian Cui, 2021. "Model-free feature screening via distance correlation for ultrahigh dimensional survival data," Statistical Papers, Springer, vol. 62(6), pages 2711-2738, December.

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