Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Bayesiansk romlig etterslepmodell× | Bayesiansk Spatial Durbin-modell× | |
|---|---|---|
| Fagfelt | Romlig analyse | Romlig analyse |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 1997 | 2009 |
| Opphavsperson≠ | LeSage (1997); fully elaborated in LeSage & Pace (2009) | LeSage & Pace |
| Type | Bayesian spatial regression | Bayesian spatial regression |
| Opprinnelig kilde | 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 |
| Alias | Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM | Bayesian SDM, Bayesian spatial lag-X model, Bayesian SDM with spatially lagged covariates, BSDM |
| Relaterte≠ | 5 | 6 |
| Sammendrag≠ | 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. | The Bayesian Spatial Durbin Model (BSDM) estimates a spatial regression that simultaneously includes a spatially lagged outcome variable and spatially lagged covariates, using Bayesian inference with Markov Chain Monte Carlo sampling. It captures both endogenous and exogenous spatial spillovers while providing full posterior distributions for all parameters, quantifying uncertainty beyond what classical maximum-likelihood estimation offers. |
| ScholarGateDatasett ↗ |
|
|