Nonparametric independence screening in sparse ultra-high-dimensional additive models

Jianqing Fan Yang Feng Rui Song

Statistics Theory and Methods mathscidoc:1912.43269

Journal of the American Statistical Association, 106, (494), 544-557, 2011.6
A variable screening procedure via correlation learning was proposed by Fan and Lv (2008) to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening (NIS) is a specific type of sure independence screening. We propose several closely related variable screening procedures. We show that with general nonparametric models, under some mild technical conditions, the proposed independence screening methods have a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, we also propose a data-driven thresholding and an iterative nonparametric independence
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@inproceedings{jianqing2011nonparametric,
  title={Nonparametric independence screening in sparse ultra-high-dimensional additive models},
  author={Jianqing Fan, Yang Feng, and Rui Song},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113404807877829},
  booktitle={Journal of the American Statistical Association},
  volume={106},
  number={494},
  pages={544-557},
  year={2011},
}
Jianqing Fan, Yang Feng, and Rui Song. Nonparametric independence screening in sparse ultra-high-dimensional additive models. 2011. Vol. 106. In Journal of the American Statistical Association. pp.544-557. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113404807877829.
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