方法对比
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| 贝叶斯多尺度地理加权回归× | 地理加权回归 (GWR)× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2017-2020 | 2002 |
| 提出者≠ | Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors | Fotheringham, Brunsdon & Charlton |
| 类型≠ | Spatially varying coefficient regression | Local spatial regression |
| 开创性文献≠ | Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| 别名 | Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| 相关≠ | 6 | 5 |
| 摘要≠ | Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterior sampling, yielding full uncertainty quantification for every local coefficient surface. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
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