A new nonparametric screening method for ultrahigh-dimensional survival data

Yanyan Liu, Jing Zhang, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


For ultrahigh-dimensional data, sure independent screening methods can effectively reduce the dimensionality while ensuring that all the active variables can be retained with high probability. However, most existing screening procedures are developed for ultrahigh-dimensional complete data and cannot be applicable to censored survival data. To address the new challenges from censoring, a novel model-free screening method was proposed through the Kolmogorov–Smirnov test statistic that is specially tailored to the ultrahigh-dimensional survival data. The sure screening property was established under some mild regularity conditions, and its superior performance over existing screening methods is demonstrated by our extensive simulation studies. A real data example of gene expression is used to illustrate the application of the proposed fully nonparametric screening procedure.
Original languageEnglish
Pages (from-to)74-85
Number of pages12
JournalComputational Statistics and Data Analysis
Publication statusPublished - 1 Mar 2018


  • Model-free
  • Sure screening property
  • Ultrahigh-dimensional survival data
  • Variable screening

ASJC Scopus subject areas

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


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