Nonparametric independence screening in sparse ultra-high-dimensional varying coefficient models

Jianqing Fan Yunbei Ma Wei Dai

Statistics Theory and Methods mathscidoc:1912.43296

Journal of the American Statistical Association, 109, (507), 1270-1284, 2014.7
The varying coefficient model is an important class of nonparametric statistical model, which allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this article, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high-dimensional varying coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS (INIS) methods are
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@inproceedings{jianqing2014nonparametric,
  title={Nonparametric independence screening in sparse ultra-high-dimensional varying coefficient models},
  author={Jianqing Fan, Yunbei Ma, and Wei Dai},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113541594379856},
  booktitle={Journal of the American Statistical Association},
  volume={109},
  number={507},
  pages={1270-1284},
  year={2014},
}
Jianqing Fan, Yunbei Ma, and Wei Dai. Nonparametric independence screening in sparse ultra-high-dimensional varying coefficient models. 2014. Vol. 109. In Journal of the American Statistical Association. pp.1270-1284. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113541594379856.
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