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Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors

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  • Lu, Jun
  • Lin, Lu

Abstract

This article investigates the feature screening procedure for multivariate response varying coefficient linear models. A new conditional canonical correlation coefficient is proposed to characterize the correlation between each predictor and the multivariate response. It is shown that the proposed method is more powerful to distinguish the informative features from the noises than the existing competitors, especially for the case of high-dimensional response. The ranking consistency and the sure screening property are established for the new method. Meanwhile, an iterative version of the feature screening procedure is also introduced. Both the numerical simulations and real data analysis are conducted to illustrate the effectiveness of our method.

Suggested Citation

  • Lu, Jun & Lin, Lu, 2018. "Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 242-254.
    • Handle: RePEc:eee:csdana:v:128:y:2018:i:c:p:242-254
    DOI: 10.1016/j.csda.2018.06.009
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    References listed on IDEAS

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    Cited by:

    1. Jun Lu & Dan Wang & Qinqin Hu, 2022. "Interaction screening via canonical correlation," Computational Statistics, Springer, vol. 37(5), pages 2637-2670, November.
    2. Ping Wang & Lu Lin, 2023. "Conditional characteristic feature screening for massive imbalanced data," Statistical Papers, Springer, vol. 64(3), pages 807-834, June.
    3. Lu, Jun & Lin, Lu & Wang, WenWu, 2021. "Partition-based feature screening for categorical data via RKHS embeddings," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

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