Censored cumulative residual independent screening for ultrahigh-dimensional survival data

Jing Zhang (), Guosheng Yin (), Yanyan Liu () and Yuanshan Wu ()
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Jing Zhang: Wuhan University
Guosheng Yin: University of Hong Kong
Yanyan Liu: Wuhan University
Yuanshan Wu: Wuhan University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 2, No 4, 273-292

Abstract: Abstract For complete ultrahigh-dimensional data, sure independent screening methods can effectively reduce the dimensionality while retaining all the active variables with high probability. However, limited screening methods have been developed for ultrahigh-dimensional survival data subject to censoring. We propose a censored cumulative residual independent screening method that is model-free and enjoys the sure independent screening property. Active variables tend to be ranked above the inactive ones in terms of their association with the survival times. Compared with several existing methods, our model-free screening method works well with general survival models, and it is invariant to the monotone transformation of the responses, as well as requiring substantially weaker moment conditions. Numerical studies demonstrate the usefulness of the censored cumulative residual independent screening method, and the new approach is illustrated with a gene expression data set.

Keywords: Cumulative residual; Model-free screening; Sure screening property; Survival data; Ultrahigh-dimensional data (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10985-017-9395-2

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