Nonparametric Independence Screening via Favored Smoothing Bandwidth
This work addresses variable selection for high-dimensional data analysis, but it appears incremental as it builds on existing nonparametric regression and screening techniques.
The authors tackled the problem of variable selection in ultrahigh-dimensional nonparametric regression by proposing a screening method based on favored smoothing bandwidth, which achieves model selection consistency and shows competitive performance in simulations and real data.
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.