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Bayesovský prostorový model chyb×Geograficky vážená regrese (GWR)×
OborProstorová analýzaProstorová analýza
RodinaRegression modelRegression model
Rok vzniku1988 (classical SEM); 2009 (Bayesian formulation)2002
TvůrceLeSage & Pace (Bayesian treatment); Anselin (classical SEM)Fotheringham, Brunsdon & Charlton
TypBayesian spatial regressionLocal spatial regression
Původní zdrojLeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Další názvyBayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error modelGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Příbuzné65
ShrnutíThe 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.
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ScholarGatePorovnat metody: Bayesian Spatial Error Model · Geographically Weighted Regression. Získáno 2026-06-15 z https://scholargate.app/cs/compare