Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская модель пространственных ошибок× | Регрессия с географически взвешенными коэффициентами (GWR)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1988 (classical SEM); 2009 (Bayesian formulation) | 2002 |
| Автор метода≠ | LeSage & Pace (Bayesian treatment); Anselin (classical SEM) | Fotheringham, Brunsdon & Charlton |
| Тип≠ | Bayesian spatial regression | Local spatial regression |
| Основополагающий источник≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Другие названия | Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Связанные≠ | 6 | 5 |
| Сводка≠ | The Bayesian Spatial Error Model (Bayesian SEM) estimates a regression in which spatially correlated disturbances are explicitly modelled through a spatial weights matrix, while all parameters — regression coefficients, spatial error autocorrelation, and error variance — receive full posterior distributions via Bayesian inference rather than point estimates. | 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. |
| ScholarGateНабор данных ↗ |
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