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Sufficient variable selection using independence measures for continuous response

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  • Yang, Baoying
  • Yin, Xiangrong
  • Zhang, Nan

Abstract

We propose two sufficient variable selection procedures, i.e., one- and two-stage approaches using independence measures for continuous response, illustrated by distance correlation and the Hilbert–Schmidt Independence Criterion correlation. We show the advantages of the proposed procedures over some existing marginal screening methods through simulations and a real data analysis. Our procedures are model-free and thus robust against model mis-specification. They are particularly useful when some active predictors are marginally independent of the response.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:173:y:2019:i:c:p:480-493
    DOI: 10.1016/j.jmva.2019.04.006
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    References listed on IDEAS

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    Cited by:

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    3. Wenjuan Li & Wenying Wang & Jingsi Chen & Weidong Rao, 2023. "Aggregate Kernel Inverse Regression Estimation," Mathematics, MDPI, vol. 11(12), pages 1-10, June.
    4. 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).
    5. Wang, Qin & Xue, Yuan, 2021. "An ensemble of inverse moment estimators for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).

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