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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The research was supported in part by the National Natural Science Foundation of China under Grant Nos. 11571112, 11501372, 11571148, 11471160, Doctoral Fund of Ministry of Education of China under Grant No. 20130076110004, Program of Shanghai Subject Chief Scientist under Grant No. 14XD1401600, and the 111 Project of China under Grant No. B14019.
This paper was recommended for publication by Editor SUN Liuquan.
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Zhang, J., Zhang, R. & Zhang, J. Feature Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Covariates. J Syst Sci Complex 31, 1350–1361 (2018). https://doi.org/10.1007/s11424-017-6310-6
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DOI: https://doi.org/10.1007/s11424-017-6310-6