A unified geographically weighted regression model
文献类型：期刊论文
| 作者 | Wu, Ying2,3; Tang, Zhipeng1; Xiong, Shifeng3 |
| 刊名 | SPATIAL STATISTICS
 |
| 出版日期 | 2023-06-01 |
| 卷号 | 55页码:25 |
| ISSN号 | 2211-6753 |
| 关键词 | Geographically weighted regression Reconstruction parameterization Spatially-varying coefficients Model selection |
| DOI | 10.1016/j.spasta.2023.100753 |
| 通讯作者 | Xiong, Shifeng(xiong@amss.ac.cn) |
| 英文摘要 | Spatial heterogeneity and spatial dependence are two cornerstones of spatial data research. It becomes more and more important to simultaneously deal with them in analyzing today's complex spatial datasets. Along this direction, we introduce a new class of geographically weighted regression models, called unified geographically weighted regression (UGWR) models, to generalize existing geographically weighted regression models. In UGWR, the regression coefficients of all covariates can vary over the space. Moreover, the dependent variable and the disturbance are considered to be spatial autoregressive, and their spatial autoregression coefficients can also vary over the space. We propose a reconstruction parameterization approach to estimate these varying coefficients. This approach can flexibly use different bandwidths to fit various smoothness degrees of the coefficients. Based on the estimators, we also provide prediction, hypothesis testing, and model selection methods under UGWR. Simulation results indicate that the UGWR model can better fit complex datasets than existing geographically weighted regression models. An empirical study on a China's regional GDP dataset also shows the effectiveness of the proposed methods. (c) 2023 Elsevier B.V. All rights reserved. |
| WOS关键词 | SPATIAL AUTOREGRESSIVE MODEL ; FALSE DISCOVERY RATE ; GENERAL FRAMEWORK ; AUTOCORRELATION ; INFERENCE |
| 资助项目 | National Key R&D Program of China[2021YFA1000300] ; National Key R&D Program of China[2021YFA1000301] ; National Key R&D Program of China[2021YFA1000303] ; National Natural Science Foundation of China[12171462] ; National Natural Science Foundation of China[41530634] |
| WOS研究方向 | Geology ; Mathematics ; Remote Sensing |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001001186400001 |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/197664]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xiong, Shifeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, KLSC, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Ying,Tang, Zhipeng,Xiong, Shifeng. A unified geographically weighted regression model[J]. SPATIAL STATISTICS,2023,55:25. |
| APA | Wu, Ying,Tang, Zhipeng,&Xiong, Shifeng.(2023).A unified geographically weighted regression model.SPATIAL STATISTICS,55,25. |
| MLA | Wu, Ying,et al."A unified geographically weighted regression model".SPATIAL STATISTICS 55(2023):25. |
入库方式： OAI收割
来源：地理科学与资源研究所
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