期刊論文
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 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120175 ) |