Kernel Knockoffs Selection for Nonparametric Additive Models
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). <80><9c>Kernel Knockoffs Selection for Nonparametric Additive Models<80><9d>. arXiv preprint .