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Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models


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  • Fan, Jianqing
  • Feng, Yang
  • Song, Rui


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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of the American Statistical Association.

Volume (Year): 106 (2011)
Issue (Month): 494 ()
Pages: 544-557

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Handle: RePEc:bes:jnlasa:v:106:i:494:y:2011:p:544-557

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Cited by:
  1. Zhaoping Hong & Yuao Hu & Heng Lian, 2013. "Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty," Metrika, Springer, vol. 76(7), pages 887-908, October.
  2. Lian, Heng, 2014. "Semiparametric Bayesian information criterion for model selection in ultra-high dimensional additive models," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 304-310.
  3. Lin, Lu & Sun, Jing & Zhu, Lixing, 2013. "Nonparametric feature screening," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 162-174.
  4. repec:wyi:journl:002212 is not listed on IDEAS
  5. Du, Pang & Cheng, Guang & Liang, Hua, 2012. "Semiparametric regression models with additive nonparametric components and high dimensional parametric components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2006-2017.
  6. Lichun Wang & Peng Lai & Heng Lian, 2013. "Polynomial spline estimation for generalized varying coefficient partially linear models with a diverging number of components," Metrika, Springer, vol. 76(8), pages 1083-1103, November.


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