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Fused mean–variance filter for feature screening

Author

Listed:
  • Yan, Xiaodong
  • Tang, Niansheng
  • Xie, Jinhan
  • Ding, Xianwen
  • Wang, Zhiqiang

Abstract

A new model-free screening approach called as the slicing fused mean–variance filter is proposed for ultrahigh dimensional data analysis. The new method has the following merits: (i) its implementation does not require specifying a regression form of predictors and response variables; (ii) it can deal with various types of covariates and response variables including continuous, discrete and categorical variables; (iii) it works well even when the covariates/random errors are heavy-tailed, or the predictors are strongly correlated, or there are outliers; (iv) it is unsensitive to the slicing scheme. Under some regularity conditions, the sure screening and ranking consistency properties are established for the proposed procedure without assuming any moment conditions on the predictors. Simulation studies are conducted to investigate the finite sample performance of the proposed procedure. A real data example is illustrated to the proposed procedure.

Suggested Citation

  • Yan, Xiaodong & Tang, Niansheng & Xie, Jinhan & Ding, Xianwen & Wang, Zhiqiang, 2018. "Fused mean–variance filter for feature screening," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 18-32.
    • Handle: RePEc:eee:csdana:v:122:y:2018:i:c:p:18-32
    DOI: 10.1016/j.csda.2017.10.008
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    References listed on IDEAS

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    Cited by:

    1. 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).
    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. Liming Wang & Xingxiang Li & Xiaoqing Wang & Peng Lai, 2022. "Unified mean-variance feature screening for ultrahigh-dimensional regression," Computational Statistics, Springer, vol. 37(4), pages 1887-1918, September.

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