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Robust model-free feature screening via quantile correlation

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  • Ma, Xuejun
  • Zhang, Jingxiao

Abstract

In this paper, we propose a new sure independence screening procedure based on quantile correlation (QC-SIS). The method not only is robust against outliers, but also can discover the nonlinear relationship between independent variables and dependent variable. We establish the sure screening property under certain technical conditions. Simulation studies are conducted to assess the performances of QC-SIS, sure independent screening (SIS), sure independent ranking and screening (SIRS), robust rank correlation screening (RRCS) and distance correlation-sure independent screening (DC-SIS). Results have shown the effectiveness and the flexibility of the proposed method. We also illustrate the QC-SIS through an empirical example.

Suggested Citation

  • Ma, Xuejun & Zhang, Jingxiao, 2016. "Robust model-free feature screening via quantile correlation," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 472-480.
    • Handle: RePEc:eee:jmvana:v:143:y:2016:i:c:p:472-480
    DOI: 10.1016/j.jmva.2015.10.010
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    References listed on IDEAS

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    1. Jingyuan Liu & Runze Li & Rongling Wu, 2014. "Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 266-274, March.
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    3. 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.
    4. 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.
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    6. Jianqing Fan & Yunbei Ma & Wei Dai, 2014. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1270-1284, September.
    7. 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.
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    Cited by:

    1. Songqiao Tang & Huiyu Wang & Guanao Yan & Lixin Zhang, 2023. "Empirical likelihood based tests for detecting the presence of significant predictors in marginal quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(2), pages 149-179, February.
    2. Sui, Meng & Rengifo, Erick W. & Court, Eduardo, 2021. "Gold, inflation and exchange rate in dollarized economies – A comparative study of Turkey, Peru and the United States," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 82-99.
    3. 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.
    4. Guo, Chaohui & Lv, Jing & Wu, Jibo, 2021. "Composite quantile regression for ultra-high dimensional semiparametric model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    5. 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.

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