This paper tackles the challenge of performing multiple quantile regressions across different quantile levels and the associated problem of controlling the familywise error rate, an issue that is generally overlooked in practice. We propose a multivariate extension of the rank-score test and embed it within a closed-testing procedure to efficiently account for multiple testing. Theoretical foundations and simulation studies demonstrate that our method effectively controls the familywise error rate while achieving higher power than traditional corrections, such as Bonferroni.

翻译：本文探讨了在不同分位数水平下进行多重分位数回归的挑战，以及控制族错误率的相关问题，该问题在实践中常被忽视。我们提出了秩得分检验的多变量扩展，并将其嵌入封闭检验程序中，以有效处理多重检验问题。理论分析和模拟研究表明，我们的方法在控制族错误率的同时，相比传统校正方法（如Bonferroni校正）具有更高的检验功效。