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Regresión Geográficamente Ponderada Bayesiana (BGWR)×Modelo de Retardo Espacial (SAR / Autoregresivo Espacial)×
CampoAnálisis espacialAnálisis espacial
FamiliaRegression modelRegression model
Año de origen20071988
Autor originalWheeler & Calder (2007); Finley (2011)Anselin (textbook formalisation); LeSage & Pace
TipoBayesian spatially varying coefficient regressionSpatial autoregressive regression
Fuente seminalFinley, 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 ↗
AliasBGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regressionSAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Relacionados55
ResumenBayesian 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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ScholarGateComparar métodos: Bayesian Geographically Weighted Regression · Spatial Lag Model. Recuperado el 2026-06-17 de https://scholargate.app/es/compare