Feature selection for varying coefficient models with ultrahigh-dimensional covariates

Jingyuan Liu, Runze Li, Rongling Wu

Research output: Contribution to journalArticlepeer-review

157 Scopus citations


This article is concerned with feature screening and variable selection for varying coefficient models with ultrahigh-dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further develop an iterative feature screening procedure. Monte Carlo simulation studies were conducted to examine the performance of the proposed procedures. In practice, we advocate a two-stage approach for varying coefficient models. The two-stage approach consists of (a) reducing the ultrahigh dimensionality by using the proposed procedure and (b) applying regularization methods for dimension-reduced varying coefficient models to make statistical inferences on the coefficient functions. We illustrate the proposed two-stage approach by a real data example. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)266-274
Number of pages9
JournalJournal of the American Statistical Association
Issue number505
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Feature selection for varying coefficient models with ultrahigh-dimensional covariates'. Together they form a unique fingerprint.

Cite this