Model-free feature screening for ultrahigh-dimensional data

Li Ping Zhu, Lexin Li, Runze Li*, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

358 Citations (Scopus)


With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis.

Original languageEnglish
Pages (from-to)1464-1475
Number of pages12
JournalJournal of the American Statistical Association
Issue number496
Publication statusPublished - Dec 2011

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Feature ranking
  • Ultrahigh-dimensional regression
  • Variable selection


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