Feature Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Covariates |
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Authors: | Junying Zhang Riquan Zhang Jiajia Zhang |
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Institution: | 1.Department of Statistics,East China Normal University,Shanghai,China;2.Department of Mathematics,Taiyuan University of Technology,Taiyuan,China;3.Department of Mathematics,Shanxi Datong University,Datong,China;4.Department of Epidemiology and Biostatistics,University of South Carolina,Columbia,USA |
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Abstract: | 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. |
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