Geographically weighted quantile regression (GWQR) has been proposed as a spatial analytical technique to simultaneously explore two heterogeneities, one of spatial heterogeneity with respect to data relationships over space and one of response heterogeneity across different locations of the outcome distribution. However, one limitation of GWQR framework is that the existing inference procedures are established based on asymptotic approximation, which may suffer computation difficulties or yield incorrect estimates with finite samples. In this article, we suggest a bootstrap approach to address this limitation. Our bootstrap enhancement is first validated by a simulation experiment and then illustrated with an empirical U.S. mortality data. The results show that the bootstrap approach provides a practical alternative for inference in GWQR and enhances the utilization of GWQR.

中文翻译：

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用地理加权分位数回归探索异质性：基于Bootstrap方法的增强

地理加权分位数回归（GWQR）已被建议作为一种空间分析技术来同时探索两种异质性，一种是关于空间数据关系的空间异质性，另一种是结果分布在不同位置的响应异质性。但是，GWQR框架的局限性在于，现有的推理过程是基于渐近逼近法建立的，这可能会遇到计算困难或使用有限样本产生不正确的估计的情况。在本文中，我们建议使用一种引导方法来解决此限制。我们的自举增强功能首先通过模拟实验进行验证，然后通过经验性美国死亡率数据进行说明。