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Independence index sufficient variable screening for categorical responses

Author

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  • Yuan, Qingcong
  • Chen, Xianyan
  • Ke, Chenlu
  • Yin, Xiangrong

Abstract

Variable screening is very popular in modern data analysis and is particularly useful for large p small n data. In this paper, a novel two-stage sufficient variable screening procedure, especially when the response is categorical is proposed. This procedure is very general and model-free, thus is robust against model mis-specification. In addition, the proposed procedure always improves existing screening approach in literature which only uses marginal relation. Asymptotic results and sure screening properties of the proposed methods are proved. Numerical studies and real data analysis are provided to demonstrate the advantages of the proposed method.

Suggested Citation

  • Yuan, Qingcong & Chen, Xianyan & Ke, Chenlu & Yin, Xiangrong, 2022. "Independence index sufficient variable screening for categorical responses," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:csdana:v:174:y:2022:i:c:s0167947322001104
    DOI: 10.1016/j.csda.2022.107530
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    References listed on IDEAS

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    1. Emre Barut & Jianqing Fan & Anneleen Verhasselt, 2016. "Conditional Sure Independence Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1266-1277, July.
    2. Lexin Li, 2007. "Sparse sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 94(3), pages 603-613.
    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. 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.
    5. 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.
    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. Xiangrong Yin & Haileab Hilafu, 2015. "Sequential sufficient dimension reduction for large p, small n problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 879-892, September.
    8. 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.
    9. Danyang Huang & Runze Li & Hansheng Wang, 2014. "Feature Screening for Ultrahigh Dimensional Categorical Data With Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 237-244, April.
    10. 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.
    11. 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.
    12. Xueqin Wang & Wenliang Pan & Wenhao Hu & Yuan Tian & Heping Zhang, 2015. "Conditional Distance Correlation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1726-1734, December.
    13. Cook, R. Dennis & Ni, Liqiang, 2005. "Sufficient Dimension Reduction via Inverse Regression: A Minimum Discrepancy Approach," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 410-428, June.
    14. 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.
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    Cited by:

    1. Ke, Chenlu & Yang, Wei & Yuan, Qingcong & Li, Lu, 2023. "Partial sufficient variable screening with categorical controls," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).

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