From regression rank scores to robust inference for censored quantile regression
From regression rank scores to robust inference for censored quantile regression
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DOI：
10.1002/cjs.11740
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Yuan Sun;Xuming He
中科院分区：
文献类型：
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作者：
Yuan Sun;Xuming He
右删失或左删失结果的分位数回归因其能够适应生存时间回归分析中的异质性而受到关注。基于排名的推理方法对于分位数回归分析具有理想的性质，但删失的数据对一般的排名概念提出了挑战。在这篇文章中，我们提出了删失分位数回归等级得分的概念，它使我们能够对单个分位数或分位数区域上的分位数回归系数构造基于排名的检验。提出了一种基于模型的Bootstrap算法来实现测试。我们还说明了在分位数回归框架中测试某些协变量的影响时，关注分位数区域而不是单个分位数水平的优势。
Quantile regression for right‐ or left‐censored outcomes has attracted attention due to its ability to accommodate heterogeneity in regression analysis of survival times. Rank‐based inferential methods have desirable properties for quantile regression analysis, but censored data poses challenges to the general concept of ranking. In this article, we propose a notion of censored quantile regression rank scores, which enables us to construct rank‐based tests for quantile regression coefficients at a single quantile or over a quantile region. A model‐based bootstrap algorithm is proposed to implement the tests. We also illustrate the advantage of focusing on a quantile region instead of a single quantile level when testing the effect of certain covariates in a quantile regression framework.