Nonparametric independence screening via favored smoothing bandwidth

Yang Feng, Yichao Wu, Leonard A. Stefanski

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalJournal of Statistical Planning and Inference
StatePublished - Dec 2018


  • Bandwidth
  • Nonparametric
  • Smoothing
  • Variable screening

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


Dive into the research topics of 'Nonparametric independence screening via favored smoothing bandwidth'. Together they form a unique fingerprint.

Cite this