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The cumulative Kolmogorov filter for model-free screening in ultrahigh dimensional data

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  • Kim, Arlene Kyoung Hee
  • Shin, Seung Jun

Abstract

We propose a cumulative Kolmogorov filter to improve the fused Kolmogorov filter proposed by Mai and Zou (2015) via cumulative slicing. We establish an improved asymptotic result under relaxed assumptions and numerically demonstrate its enhanced finite sample performance.

Suggested Citation

  • Kim, Arlene Kyoung Hee & Shin, Seung Jun, 2017. "The cumulative Kolmogorov filter for model-free screening in ultrahigh dimensional data," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 238-243.
    • Handle: RePEc:eee:stapro:v:126:y:2017:i:c:p:238-243
    DOI: 10.1016/j.spl.2017.03.012
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Zhu, Li-Ping & Zhu, Li-Xing & Feng, Zheng-Hui, 2010. "Dimension Reduction in Regressions Through Cumulative Slicing Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1455-1466.
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    Cited by:

    1. 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.

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