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| Bayesian Geographically Weighted Regression (BGWR)× | Model opóźnienia przestrzennego (SAR / Autoregresyjny przestrzenny)× | |
|---|---|---|
| Dziedzina | Analiza przestrzenna | Analiza przestrzenna |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2007 | 1988 |
| Twórca≠ | Wheeler & Calder (2007); Finley (2011) | Anselin (textbook formalisation); LeSage & Pace |
| Typ≠ | Bayesian spatially varying coefficient regression | Spatial autoregressive regression |
| Źródło pierwotne≠ | Finley, A. O. (2011). Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2(2), 143-154. DOI ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| Inne nazwy | BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertainty quantification rather than only point estimates. | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. |
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