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Nonparametric feature screening

Author

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  • Lin, Lu
  • Sun, Jing
  • Zhu, Lixing

Abstract

The measure of correlation between response and predictors plays a critical role in feature ranking and screening for nonparametric regression models. In this paper, a nonparametric function-correlative feature screening is introduced. The newly proposed method does not need any assumption on structural relationships between response and predictors, and among predictors. By using local information flows of model variables, the function-correlation between response and predictors is captured successfully. Selection consistency is achieved as well. Simulation studies are carried out to examine the performance of the new method.

Suggested Citation

  • Lin, Lu & Sun, Jing & Zhu, Lixing, 2013. "Nonparametric feature screening," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 162-174.
    • Handle: RePEc:eee:csdana:v:67:y:2013:i:c:p:162-174
    DOI: 10.1016/j.csda.2013.05.016
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Li‐Ping Zhu & Li‐Xing Zhu, 2009. "On distribution‐weighted partial least squares with diverging number of highly correlated predictors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 525-548, April.
    5. Liping Zhu & Tao Wang & Lixing Zhu & Louis Ferré, 2010. "Sufficient dimension reduction through discretization-expectation estimation," Biometrika, Biometrika Trust, vol. 97(2), pages 295-304.
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    Citations

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    Cited by:

    1. 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).
    2. Zhenghui Feng & Lu Lin & Ruoqing Zhu & Lixing Zhu, 2020. "Nonparametric variable selection and its application to additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 827-854, June.
    3. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Qiu, Debin & Ahn, Jeongyoun, 2020. "Grouped variable screening for ultra-high dimensional data for linear model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    5. Shuaishuai Chen & Jun Lu, 2023. "Quantile-Composited Feature Screening for Ultrahigh-Dimensional Data," Mathematics, MDPI, vol. 11(10), pages 1-21, May.
    6. Lin, Lu & Sun, Jing, 2016. "Adaptive conditional feature screening," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 287-301.
    7. 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.
    8. Yi Chu & Lu Lin, 2020. "Conditional SIRS for nonparametric and semiparametric models by marginal empirical likelihood," Statistical Papers, Springer, vol. 61(4), pages 1589-1606, August.
    9. Jun Lu & Lu Lin, 2020. "Model-free conditional screening via conditional distance correlation," Statistical Papers, Springer, vol. 61(1), pages 225-244, February.
    10. Qinqin Hu & Lu Lin, 2017. "Conditional sure independence screening by conditional marginal empirical likelihood," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 63-96, February.
    11. Zhou Yu & Yuexiao Dong & Li-Xing Zhu, 2016. "Trace Pursuit: A General Framework for Model-Free Variable Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 813-821, April.
    12. Peirong Xu & Lixing Zhu & Yi Li, 2014. "Ultrahigh dimensional time course feature selection," Biometrics, The International Biometric Society, vol. 70(2), pages 356-365, June.

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