IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v125y2017icp141-148.html
   My bibliography  Save this article

Model free feature screening with dependent variable in ultrahigh dimensional binary classification

Author

Listed:
  • Lai, Peng
  • Song, Fengli
  • Chen, Kaiwen
  • Liu, Zhi

Abstract

The feature screening procedure based on the expected conditional Kolmogorov filter is proposed for the ultrahigh dimensional binary classification problem with dependent variable. The sure screening and ranking consistency properties are established. Some numerical examples are also presented.

Suggested Citation

  • Lai, Peng & Song, Fengli & Chen, Kaiwen & Liu, Zhi, 2017. "Model free feature screening with dependent variable in ultrahigh dimensional binary classification," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 141-148.
    • Handle: RePEc:eee:stapro:v:125:y:2017:i:c:p:141-148
    DOI: 10.1016/j.spl.2017.02.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715217300603
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2017.02.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Wang, Lifeng & Li, Hongzhe & Huang, Jianhua Z., 2008. "Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1556-1569.
    3. Hengjian Cui & Runze Li & Wei Zhong, 2015. "Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 630-641, June.
    4. 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.
    5. Qing Mai & Hui Zou, 2013. "The Kolmogorov filter for variable screening in high-dimensional binary classification," Biometrika, Biometrika Trust, vol. 100(1), pages 229-234.
    6. Lai, Peng & Liu, Yiming & Liu, Zhi & Wan, Yi, 2017. "Model free feature screening for ultrahigh dimensional data with responses missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 201-216.
    7. 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.
    8. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fengli Song & Peng Lai & Baohua Shen, 2020. "Robust composite weighted quantile screening for ultrahigh dimensional discriminant analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 799-820, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Xingxiang & Cheng, Guosheng & Wang, Liming & Lai, Peng & Song, Fengli, 2017. "Ultrahigh dimensional feature screening via projection," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 88-104.
    2. Liming Wang & Xingxiang Li & Xiaoqing Wang & Peng Lai, 2022. "Unified mean-variance feature screening for ultrahigh-dimensional regression," Computational Statistics, Springer, vol. 37(4), pages 1887-1918, September.
    3. Zhao, Shaofei & Fu, Guifang, 2022. "Distribution-free and model-free multivariate feature screening via multivariate rank distance correlation," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    4. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
    5. Dai, Linlin & Chen, Kani & Sun, Zhihua & Liu, Zhenqiu & Li, Gang, 2018. "Broken adaptive ridge regression and its asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 334-351.
    6. Tang, Niansheng & Xia, Linli & Yan, Xiaodong, 2019. "Feature screening in ultrahigh-dimensional partially linear models with missing responses at random," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 208-227.
    7. Sheng, Ying & Wang, Qihua, 2020. "Model-free feature screening for ultrahigh dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    8. Sweata Sen & Damitri Kundu & Kiranmoy Das, 2023. "Variable selection for categorical response: a comparative study," Computational Statistics, Springer, vol. 38(2), pages 809-826, June.
    9. 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.
    10. Yang, Baoying & Yin, Xiangrong & Zhang, Nan, 2019. "Sufficient variable selection using independence measures for continuous response," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 480-493.
    11. Ke, Chenlu & Yang, Wei & Yuan, Qingcong & Li, Lu, 2023. "Partial sufficient variable screening with categorical controls," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    12. Zhong, Wei & Wang, Jiping & Chen, Xiaolin, 2021. "Censored mean variance sure independence screening for ultrahigh dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    13. Fengli Song & Peng Lai & Baohua Shen, 2020. "Robust composite weighted quantile screening for ultrahigh dimensional discriminant analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 799-820, October.
    14. Xiang-Jie Li & Xue-Jun Ma & Jing-Xiao Zhang, 2017. "Robust feature screening for varying coefficient models via quantile partial correlation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 17-49, January.
    15. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    16. Zhang, Jing & Wang, Qihua & Kang, Jian, 2020. "Feature screening under missing indicator imputation with non-ignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    17. Zakariya Yahya Algamal & Muhammad Hisyam Lee, 2019. "A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 753-771, September.
    18. Chen, Xiaolin & Chen, Xiaojing & Wang, Hong, 2018. "Robust feature screening for ultra-high dimensional right censored data via distance correlation," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 118-138.
    19. Qinqin Hu & Lu Lin, 2022. "Feature Screening in High Dimensional Regression with Endogenous Covariates," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 949-969, October.
    20. Akira Shinkyu, 2023. "Forward Selection for Feature Screening and Structure Identification in Varying Coefficient Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 485-511, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:125:y:2017:i:c:p:141-148. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.