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Bayesovska geografski ponderirana regresija (BGWR)×Model prostornog zaostajanja (SAR / Prostorni autoregresijski)×
PodručjeProstorna analizaProstorna analiza
ObiteljRegression modelRegression model
Godina nastanka20071988
TvoracWheeler & Calder (2007); Finley (2011)Anselin (textbook formalisation); LeSage & Pace
VrstaBayesian spatially varying coefficient regressionSpatial autoregressive regression
Temeljni izvorFinley, 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 ↗
Drugi naziviBGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regressionSAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Srodne55
SažetakBayesian 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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ScholarGateUsporedite metode: Bayesian Geographically Weighted Regression · Spatial Lag Model. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare