Chen Vivian Yi-Ju, Yang Tse-Chuan, Matthews Stephen A
Department of Statistics, Tamkang University, Taipei, Taiwan.
Department of Sociology, University at Albany, State University of New York, 315 AS, 1400 Washington Avenue, Albany, NY 12222.
Geogr Anal. 2020 Oct;52(4):642-661. doi: 10.1111/gean.12229. Epub 2020 Feb 11.
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 paper, 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 US mortality data. The results show that the bootstrap provides a practical alternative for inference in GWQR and enhances the utilization of GWQR.
地理加权分位数回归（GWQR）已被提出作为一种空间分析技术，用于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，另一种是结果分布不同位置的响应异质性。然而，GWQR框架的一个局限性是，现有的推断程序是基于渐近近似建立的，这在有限样本情况下可能会遇到计算困难或产生错误估计。在本文中，我们提出了一种自助法来解决这一局限性。我们的自助法改进首先通过模拟实验进行验证，然后用美国死亡率实证数据进行说明。结果表明，自助法为GWQR推断提供了一种实用的替代方法，并提高了GWQR的利用率。
