Salīdzināt metodes

	Beijesa telpiskās kļūdu modelis	Moran's I
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1988 (classical SEM); 2009 (Bayesian formulation)	1950
Autors	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)	Patrick A. P. Moran
Tips	Bayesian spatial regression	Spatial autocorrelation statistic
Pirmavots	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Citi nosaukumi	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model	Moran's I statistic, global Moran's I, spatial autocorrelation index, Moran index
Saistītās	6	6
Kopsavilkums	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.	Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differs from complete spatial randomness with a single, interpretable number.