A new nonparametric screening method for ultrahigh-dimensional survival data

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.