Nonparametric feature screening

Lu Lin*, Jing Sun, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

The measure of correlation between response and predictors plays a critical role in feature ranking and screening for nonparametric regression models. In this paper, a nonparametric function-correlative feature screening is introduced. The newly proposed method does not need any assumption on structural relationships between response and predictors, and among predictors. By using local information flows of model variables, the function-correlation between response and predictors is captured successfully. Selection consistency is achieved as well. Simulation studies are carried out to examine the performance of the new method.

Original languageEnglish
Pages (from-to)162-174
Number of pages13
JournalComputational Statistics and Data Analysis
Volume67
DOIs
Publication statusPublished - 2013

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Feature screening
  • Function-correlation
  • Marginal utility
  • Nonparametric model
  • Ultrahigh-dimensional data

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