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

ZHANG Junying · ZHANG Riquan · ZHANG Jiajia

Journal of Systems Science & Complexity ›› 2018, Vol. 31 ›› Issue (5) : 1350-1361.

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PDF(198 KB)
Journal of Systems Science & Complexity ›› 2018, Vol. 31 ›› Issue (5) : 1350-1361. DOI: 10.1007/s11424-017-6310-6

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

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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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ZHANG Junying · ZHANG Riquan · ZHANG Jiajia. Feature Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Covariates. Journal of Systems Science and Complexity, 2018, 31(5): 1350-1361 https://doi.org/10.1007/s11424-017-6310-6
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