The Kolmogorov filter for variable screening in high-dimensional binary classification

Qing Mai, Hui Zou

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


Variable screening techniques have been proposed to mitigate the impact of high dimensionality in classification problems, including t-test marginal screening (Fan & Fan, 2008) and maximum marginal likelihood screening (Fan & Song, 2010). However, these methods rely on strong modelling assumptions that are easily violated in real applications. To circumvent the parametric modelling assumptions, we propose a new variable screening technique for binary classification based on the Kolmogorov-Smirnov statistic. We prove that this so-called Kolmogorov filter enjoys the sure screening property under much weakened model assumptions. We supplement our theoretical study by a simulation study.

Original languageEnglish (US)
Pages (from-to)229-234
Number of pages6
Issue number1
StatePublished - Mar 1 2013


  • Dvoretzky-Kiefer-Wolfowitz inequality
  • Kolmogorov-Smirnov test
  • Sure screening property

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