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| 베이즈 공간 회귀× | 공간 오차 모형(SEM)× | |
|---|---|---|
| 분야 | 공간분석 | 공간분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1990s–2000s | 1988 |
| 창시자≠ | Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors | Anselin |
| 유형≠ | Bayesian hierarchical regression | Spatial regression (spatially autocorrelated errors) |
| 원전≠ | Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| 별칭 | Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model | SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error) |
| 관련≠ | 3 | 5 |
| 요약≠ | Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates small or irregular spatial datasets via hierarchical priors. | The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares. |
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