Journal of Guangxi Normal University(Natural Science Edition) ›› 2021, Vol. 39 ›› Issue (6): 99-111.doi: 10.16088/j.issn.1001-6600.2020122406

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Quantile Feature Screening for Ultra High Dimensional Censored Data

TIAN Zhentao, ZHANG Junjian*   

  1. School of Mathematics and Statistics, Guangxi Normal University, Guilin Guangxi 541006, China
  • Received:2020-12-24 Revised:2021-03-30 Online:2021-11-25 Published:2021-12-08

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Abstract: In this paper, conditional quantiles are used to study the feature screening problem of ultra-high dimensional data when response variables are randomly censored and nonrandomly censored, respeclively. Then, a corresponding feature screening method is proposed. Through theoretical and simulation study, it is verified that the variable set created by this method satisfies the sure screening and ranking consistency properties. Compared with the existing methods, this method has some advantages when covariates and censored variables are correlative.

Key words: ultra-high dimensional censoring data, feature screening, conditional quantile

CLC Number: 

  • O212.1
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