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Model-free variable selection for conditional mean in regression

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  • Dong, Yuexiao
  • Yu, Zhou
  • Zhu, Liping

Abstract

A novel test statistic is proposed to identify important predictors for the conditional mean function in regression. The stepwise regression algorithm based on the proposed test statistic guarantees variable selection consistency without specifying the functional form of the conditional mean. When the predictors are ultrahigh dimensional, a model-free screening procedure is introduced to precede the stepwise regression algorithm. The screening procedure has the sure screening property when the number of predictors grows at an exponential rate of the available sample size. The finite-sample performances of our proposals are demonstrated via numerical studies.

Suggested Citation

  • Dong, Yuexiao & Yu, Zhou & Zhu, Liping, 2020. "Model-free variable selection for conditional mean in regression," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:csdana:v:152:y:2020:i:c:s016794732030133x
    DOI: 10.1016/j.csda.2020.107042
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    References listed on IDEAS

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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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