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Feature screening for multiple responses

Author

Listed:
  • Jiang, Zhenzhen
  • Guo, Hongping
  • Wang, Jinjuan

Abstract

Feature screening has been widely investigated in many literatures and quite a few procedures have been proposed. However, most of the existing methods are developed based on regularization strategy and model assumptions such as linear model and Gaussian distribution, which limit their application range. And also, they were mainly designed to deal with univariate response and cannot handle multiple responses situations. To tackle these issues, we introduce a new association measure for multiple responses and univariate predictor, called multiple explained variability (MEV), and further propose a feature screening procedure, named MEV-SIS, based on MEV. MEV-SIS removes the commonly used model assumptions and can conduct feature screening for multiple responses simultaneously. The asymptotic properties of MEV are deduced, and the sure screening property and ranking consistency property of MEV-SIS are obtained. Extensive simulation studies and real data application demonstrate the advantage of MEV-SIS over the existing screening procedures in sufficiency and robustness.

Suggested Citation

  • Jiang, Zhenzhen & Guo, Hongping & Wang, Jinjuan, 2023. "Feature screening for multiple responses," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:jmvana:v:198:y:2023:i:c:s0047259x23000696
    DOI: 10.1016/j.jmva.2023.105223
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    References listed on IDEAS

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    1. Sheng, Ying & Wang, Qihua, 2020. "Model-free feature screening for ultrahigh dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    2. Fan, Jianqing & Feng, Yang & Song, Rui, 2011. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 544-557.
    3. Qu, Lianqiang & Wang, Xiaoyu & Sun, Liuquan, 2022. "Variable screening for varying coefficient models with ultrahigh-dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    4. Shurong Zheng & Ning-Zhong Shi & Zhengjun Zhang, 2012. "Generalized Measures of Correlation for Asymmetry, Nonlinearity, and Beyond," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1239-1252, September.
    5. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    6. Runze Li & Wei Zhong & Liping Zhu, 2012. "Feature Screening via Distance Correlation Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1129-1139, September.
    7. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    8. Yang, Guangren & Zhang, Ling & Li, Runze & Huang, Yuan, 2019. "Feature screening in ultrahigh-dimensional varying-coefficient Cox model," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 284-297.
    9. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    10. Min Chen & Yimin Lian & Zhao Chen & Zhengjun Zhang, 2017. "Sure explained variability and independence screening," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 849-883, October.
    11. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    12. Kun Chen & Hongbo Dong & Kung-Sik Chan, 2013. "Reduced rank regression via adaptive nuclear norm penalization," Biometrika, Biometrika Trust, vol. 100(4), pages 901-920.
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