Nonparametric screening and feature selection for ultrahigh-dimensional Case II interval-censored failure time data.

ABSTRACT: For the analysis of ultrahigh-dimensional data, the first step is often to perform screening and feature selection to effectively reduce the dimensionality while retaining all the active or relevant variables with high probability. For this, many methods have been developed under various frameworks but most of them only apply to complete data. In this paper, we consider an incomplete data situation, case II interval-censored failure time data, for which there seems to be no screening procedure. Basing on the idea of cumulative residual, a model-free or nonparametric method is developed and shown to have the sure independent screening property. In particular, the approach is shown to tend to rank the active variables above the inactive ones in terms of their association with the failure tim

SUBMITTER: Hu Q 

PROVIDER: S-EPMC7988961 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature