kko

Description

A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.

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Maintainer: ‘Xiang Lyu <xianglyu@berkeley.edu>’

The Description field contains
  Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Xiang Lyu <xianglyu@berkeley.edu>’

The Description field contains
  Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Xiang Lyu <xianglyu@berkeley.edu>’

The Description field contains
  Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Xiang Lyu <xianglyu@berkeley.edu>’

The Description field contains
  Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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