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A quantile-slicing approach for sufficient dimension reduction with censored responses

Authors
Kim, HyungwooShin, Seung Jun
Issue Date
1월-2021
Publisher
WILEY
Keywords
censored kernel quantile regression; dimension reduction; time-to-event data
Citation
BIOMETRICAL JOURNAL, v.63, no.1, pp.201 - 212
Indexed
SCIE
SCOPUS
Journal Title
BIOMETRICAL JOURNAL
Volume
63
Number
1
Start Page
201
End Page
212
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/50639
DOI
10.1002/bimj.201900250
ISSN
0323-3847
Abstract
Sufficient dimension reduction (SDR) that effectively reduces the predictor dimension in regression has been popular in high-dimensional data analysis. Under the presence of censoring, however, most existing SDR methods suffer. In this article, we propose a new algorithm to perform SDR with censored responses based on the quantile-slicing scheme recently proposed by Kim et al. First, we estimate the conditional quantile function of the true survival time via the censored kernel quantile regression (Shin et al.) and then slice the data based on the estimated censored regression quantiles instead of the responses. Both simulated and real data analysis demonstrate promising performance of the proposed method.
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