Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Bayesiansk romlig feilmodell× | Bayesiansk Spatial Durbin-modell× | |
|---|---|---|
| Fagfelt | Romlig analyse | Romlig analyse |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 1988 (classical SEM); 2009 (Bayesian formulation) | 2009 |
| Opphavsperson≠ | LeSage & Pace (Bayesian treatment); Anselin (classical SEM) | 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 SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model | Bayesian SDM, Bayesian spatial lag-X model, Bayesian SDM with spatially lagged covariates, BSDM |
| Relaterte | 6 | 6 |
| Sammendrag≠ | 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 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 ↗ |
|
|