Doubly truncated data arise when the event time of interest T is observed only if it falls within a subject-specific, possibly random, interval . In this article, we study the problem of fitting a quantile regression model with doubly truncated data. Based on the non-parametric maximum likelihood estimator of , we proposed a weighted quantile regression estimator. Our method leads to a simple algorithm that can be conveniently implemented with R software. We show that the proposed estimator is consistent and asymptotically normal under appropriate conditions. We evaluate the finite sample performance of the proposed estimators through simulation studies. The proposed method is illustrated using AIDS blood transfusion data.

中文翻译：

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双截断数据的分位数回归

仅当感兴趣的事件时间 T 落入特定主题（可能是随机的）区间内时，才会出现双重截断数据。在本文中，我们研究了用双截断数据拟合分位数回归模型的问题。基于 的非参数最大似然估计量，我们提出了加权分位数回归估计量。我们的方法导致了一个简单的算法，可以方便地用 R 软件实现。我们表明，在适当的条件下，所提出的估计量是一致且渐近正态的。我们通过模拟研究评估了所提出的估计器的有限样本性能。使用艾滋病输血数据说明了所提出的方法。