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| ベイズ空間誤差モデル× | ベイズ空間ラグモデル× | |
|---|---|---|
| 分野 | 空間分析 | 空間分析 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1988 (classical SEM); 2009 (Bayesian formulation) | 1997 |
| 提唱者≠ | LeSage & Pace (Bayesian treatment); Anselin (classical SEM) | LeSage (1997); fully elaborated in LeSage & Pace (2009) |
| 種類 | Bayesian spatial regression | Bayesian spatial regression |
| 原典 | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 |
| 別名 | Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model | Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM |
| 関連≠ | 6 | 5 |
| 概要≠ | The 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. | The Bayesian Spatial Lag Model (BSLM) extends the classical spatial autoregressive (SAR) regression by placing prior distributions over all parameters and recovering full posterior distributions via MCMC sampling. It explicitly accounts for spatial dependence — the outcome in one location is partly driven by outcomes in neighboring locations — and yields uncertainty-quantified estimates of both regression coefficients and the spatial autocorrelation parameter rho. |
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