Journal
BIOMETRIKA
Volume 100, Issue 1, Pages 229-234Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/ass062
Keywords
Dvoretzky-Kiefer-Wolfowitz inequality; Kolmogorov-Smirnov test; Sure screening property
Funding
- National Science Foundation, U.S.A.
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0846068] Funding Source: National Science Foundation
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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.
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