论文标题
在变化回归分位数的时间内识别识别
Shift identification in time varying regression quantiles
论文作者
论文摘要
本文研究了与水平转移是否相同的分位数回归曲线是否相同。允许回归模型中涉及的错误和协变量是局部固定的。我们在相应的非参数假设检验问题中对此问题进行了正式化，并开发了基于集成的平方测试（SIT）以及同时置信频段（SCB）方法。在无效和局部替代方案下，SIT和SCB的渐近特性得出了。此外，当比较的数据集取决于时，还研究了这些测试的渐近特性。然后，我们提出有效的野生引导算法来实现SIT和SCB。此外，通过分析与COVID-19-19爆发和气候科学相关的模拟和真实数据来说明所提出方法的实用性。
This article investigates whether time-varying quantile regression curves are the same up to the horizontal shift or not. The errors and the covariates involved in the regression model are allowed to be locally stationary. We formalize this issue in a corresponding non-parametric hypothesis testing problem, and develop an integrated-squared-norm based test (SIT) as well as a simultaneous confidence band (SCB) approach. The asymptotic properties of SIT and SCB under null and local alternatives are derived. Moreover, the asymptotic properties of these tests are also studied when the compared data sets are dependent. We then propose valid wild bootstrap algorithms to implement SIT and SCB. Furthermore, the usefulness of the proposed methodology is illustrated via analysing simulated and real data related to COVID-19 outbreak and climate science.
