Adaptively robust geographically weighted regression

被引:7
作者
Sugasawa, Shonosuke [1 ,3 ]
Murakami, Daisuke [2 ]
机构
[1] Univ Tokyo, Ctr Spatial Informat Sci, Tokyo, Japan
[2] Inst Stat Math, Dept Stat Data Sci, Tokyo, Japan
[3] 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan
基金
日本学术振兴会;
关键词
Majorization-Minimization algorithm; Robust divergence; Outliers; PARAMETER; SELECTION; MODELS;
D O I
10.1016/j.spasta.2022.100623
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on gamma-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tuned in a data-dependent manner. Further, the proposed method can produce robust standard error estimates and give us a reasonable quantity for local outlier detection. We demonstrate that the proposed method is superior to the existing robust geographically weighted regression through simulation and data analysis. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:14
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