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.

中文翻译：

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一种通过删失响应进行充分降维的分位数切片方法

有效降低回归中的预测维度的足够降维（SDR）已经在高维数据分析中流行。然而，在审查的存在下，大多数现有的 SDR 方法都会受到影响。在本文中，我们提出了一种新算法，基于 Kim 等人最近提出的分位数切片方案，使用审查响应执行 SDR。首先，我们通过删失核分位数回归（Shin 等人）估计真实生存时间的条件分位数函数，然后根据估计的删失回归分位数而不是响应对数据进行切片。模拟和真实数据分析都证明了所提出方法的良好性能。