Quantile treatment effects are often considered in a quantile regression framework to adjust for the effect of covariates. In this study, we focus on the problem of testing whether the treatment effect is significant at a set of quantile levels (e.g. lower quantiles). We propose a regional quantile regression rank test as a generalisation of the rank test at an individual quantile level. This test statistic allows us to detect the treatment effect for a prespecified quantile interval by integrating the regression rank scores over the region of interest. A new model-based bootstrap method is constructed to estimate the null distribution of the test statistic. A simulation study is conducted to demonstrate the validity and usefulness of the proposed test. We also demonstrate the use of the proposed method through an analysis of the 2016 US birth weight data and selected S&P 500 sector portfolio data.

通常在分位数回归框架中考虑分位数治疗效果以调整协变量的效果。在这项研究中，我们专注于测试治疗效果在一组分位数水平（例如较低的分位数）上是否显着的问题。我们提出了一个区域分位数回归秩检验作为个体分位数水平秩检验的概括。该检验统计量使我们能够通过对感兴趣区域的回归排名分数进行积分来检测预先指定的分位数区间的治疗效果。构建了一种新的基于模型的 bootstrap 方法来估计检验统计量的零分布。进行模拟研究以证明所提出的测试的有效性和有用性。