Journal of Systems Science and Complexity

, Volume 31, Issue 5, pp 1350–1361 | Cite as

Feature Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Covariates

  • Junying ZhangEmail author
  • Riquan Zhang
  • Jiajia Zhang


This paper considers the feature screening and variable selection for ultrahigh dimensional covariates. The new feature screening procedure base on conditional expectation which is used to differentiate whether an explanatory variable contributes to a response variable or not, without requiring a specific parametric form of the underlying data model. The authors estimate the marginal conditional expectation by kernel regression estimator. The proposed method is showed to have sure screen property. The authors propose an iterative kernel estimator algorithm to reduce the ultrahigh dimensionality to an appropriate scale. Simulation results and real data analysis demonstrate the proposed method works well and performs better than competing methods.


Conditional expectation dimensionality reduction nonparametric and semiparametric models ultrahigh dimension variable screening 


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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsEast China Normal UniversityShanghaiChina
  2. 2.Department of MathematicsTaiyuan University of TechnologyTaiyuanChina
  3. 3.Department of MathematicsShanxi Datong UniversityDatongChina
  4. 4.Department of Epidemiology and BiostatisticsUniversity of South CarolinaColumbiaUSA

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