Exploring Heterogeneities with Geographically Weighted Quantile Regression: An Enhancement Based on the Bootstrap Approach
學年 109
學期 1
出版（發表）日期 2020-10-11
作品名稱 Exploring Heterogeneities with Geographically Weighted Quantile Regression: An Enhancement Based on the Bootstrap Approach
作品名稱（其他語言）
著者 Vivian Yi‐Ju Chen; Tse‐Chuan Yang; Stephen A. Matthews
單位
出版者
著錄名稱、卷期、頁數 Geographical Analysis 52(4), p.642-661
摘要 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.
關鍵字
語言 en
ISSN 1538-4632
期刊性質 國外
收錄於 SSCI
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版,紙本
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