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Nonparametric Independence Screening via Favored Smoothing Bandwidth
journal contributionposted on 2018-06-19, 00:00 authored by Yang Feng, Yichao Wu, Leonard Stefanski
We propose a flexible nonparametric regression method for ultrahigh-dimensional data. As a first step, we propose a fast screening method based on the favored smoothing bandwidth of the marginal local constant regression. Then, an iterative procedure is developed to recover both the important covariates and the regression function. Theoretically, we prove that the favored smoothing bandwidth based screening possesses the model selection consistency property. Simulation studies as well as real data analysis show the competitive performance of the new procedure
Y. Feng was funded by NSF CAREER grant DMS-1554804; Y. Wu by NSF grant DMS-1055210 and NIH grant P01CA142538; and L. Stefanski by NSF grant DMS-1406456, NIH grants R01CA085848 and P01CA142538.
CitationFeng, Y., Wu, Y. and Stefanski, L. A. Nonparametric independence screening via favored smoothing bandwidth. Journal of Statistical Planning and Inference. 2017. 10.1016/j.jspi.2017.11.006