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

Hyungwoo Kim, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalBiometrical Journal
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • censored kernel quantile regression
  • dimension reduction
  • time-to-event data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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