This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix W and the autocorrelation parameter . We provide an interpretation of the covariances between the random variables observed at two spatial units, based on a particular type of walks connecting the two units. The interpretation serves to explain a number of correlation properties of SAR(1) models, and clarifies why it is impossible to control the correlations through the specification of W
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an ...
The AR(1) and power models of spatial correlation are popular in the analysis of field trial data. ...
Least squares estimation has casually been dismissed as an inconsistent estimation method for mixed ...
This paper investigates how the correlations implied by a first-order simultaneous autoregressive (S...
This paper investigates how the correlations implied by a first-order simultaneous au-toregressive (...
This paper investigates how the correlations implied by a first-order simultaneous autoregressive (...
This paper investigates how the correlations implied by a first-order simultaneous au-toregressive (...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
This paper studies the correlation structure of spatial autoregressions defined over arbitrary confi...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
Spatial autocorrelation is an assessment of the correlation between two random variables which descr...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressi...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an ...
The AR(1) and power models of spatial correlation are popular in the analysis of field trial data. ...
Least squares estimation has casually been dismissed as an inconsistent estimation method for mixed ...
This paper investigates how the correlations implied by a first-order simultaneous autoregressive (S...
This paper investigates how the correlations implied by a first-order simultaneous au-toregressive (...
This paper investigates how the correlations implied by a first-order simultaneous autoregressive (...
This paper investigates how the correlations implied by a first-order simultaneous au-toregressive (...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
This paper studies the correlation structure of spatial autoregressions defined over arbitrary confi...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
Spatial autocorrelation is an assessment of the correlation between two random variables which descr...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressi...
We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weigh...
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an ...
The AR(1) and power models of spatial correlation are popular in the analysis of field trial data. ...
Least squares estimation has casually been dismissed as an inconsistent estimation method for mixed ...