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Model-free sure screening via maximum correlation

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  • Huang, Qiming
  • Zhu, Yu

Abstract

For screening features in an ultrahigh-dimensional setting, we develop a maximum correlation-based sure independence screening (MC-SIS) procedure, and show that MC-SIS possesses the sure screen property without imposing model or distributional assumptions on the response and predictor variables. MC-SIS is a model-free method in contrast with some other existing model-based sure independence screening methods in the literature. Simulation examples and a real data application are used to demonstrate the performance of MC-SIS and to compare MC-SIS with other existing sure screening methods. The results show that MC-SIS can outperform those methods when their model assumptions are violated, and remain competitive when the model assumptions are satisfied.

Suggested Citation

  • Huang, Qiming & Zhu, Yu, 2016. "Model-free sure screening via maximum correlation," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 89-106.
    • Handle: RePEc:eee:jmvana:v:148:y:2016:i:c:p:89-106
    DOI: 10.1016/j.jmva.2016.02.014
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    References listed on IDEAS

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    Cited by:

    1. Tamás F. Móri & Gábor J. Székely, 2019. "Four simple axioms of dependence measures," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 1-16, January.
    2. Sang, Yongli & Dang, Xin, 2024. "Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation," Journal of Multivariate Analysis, Elsevier, vol. 204(C).

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