Nonparametric independence feature screening for ultrahigh-dimensional survival data

Jing Pan (), Yuan Yu and Yong Zhou
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Jing Pan: Shanghai University of Finance and Economics
Yuan Yu: Shanghai University of Finance and Economics
Yong Zhou: East China Normal University

Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 7, No 4, 847 pages

Abstract: Abstract With the explosion of digital information, high-dimensional data is frequently collected in prevalent domains, in which the dimension of covariates can be much larger than the sample size. Many effective methods have been developed to reduce the dimension of such data recently, however, few methods might perform well for survival data with censoring. In this article, we develop a novel nonparametric feature screening procedure based on ultrahigh-dimensional survival data by incorporating the inverse probability weighting scheme to tackle the issue of censoring. The proposed method is model-free and hence can be implemented for extensive survival models. Moreover, it is robust to heterogeneity and invariant to monotone increasing transformations of the response. The sure screening property and ranking consistency property are also established under mild conditions. The competence and robustness of our method is further confirmed through comprehensive simulation studies and an analysis of a real data example.

Keywords: Consistency in ranking; Feature screening; High-dimensional data; Right censoring; Sure screening property (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00184-018-0660-5

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