This paper investigates how the correlations implied by a first-order simultaneous au-toregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In par-ticular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on graph distance, and the behavior of the correlations at the extremes of the parameter space. Throughout the analysis differences between directed and undirected networks are emphasized. The graph theoretic representation also clarifies why it is difficult to relate propert...
We propose a rate equation approach to compute two vertex correlations in scale-free growing network...
This paper extends the results of canonical correlation analysis of Anderson [2002. Canonical correl...
In epidemiological settings, we are often faced with numerous short time series, and a parsimonious ...
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 autoregressive (S...
This paper investigates how the correlations implied by a first-order simultaneous au-toregressive (...
This paper studies the correlation structure of spatial autoregressions defined over arbitrary confi...
summary:An explicit formula for the correlation coefficient in a two-dimensional AR(1) process is de...
The AR(1) and power models of spatial correlation are popular in the analysis of field trial data. ...
In epidemiological settings, we are often faced with numerous short time series, and a parsimonious ...
This document clarifies the use of Moran’s autocorrelation coefficient to quantify whether the distr...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an ...
This short paper proves inequalities that restrict the magnitudes of the partial correlations in sta...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
We propose a rate equation approach to compute two vertex correlations in scale-free growing network...
This paper extends the results of canonical correlation analysis of Anderson [2002. Canonical correl...
In epidemiological settings, we are often faced with numerous short time series, and a parsimonious ...
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 autoregressive (S...
This paper investigates how the correlations implied by a first-order simultaneous au-toregressive (...
This paper studies the correlation structure of spatial autoregressions defined over arbitrary confi...
summary:An explicit formula for the correlation coefficient in a two-dimensional AR(1) process is de...
The AR(1) and power models of spatial correlation are popular in the analysis of field trial data. ...
In epidemiological settings, we are often faced with numerous short time series, and a parsimonious ...
This document clarifies the use of Moran’s autocorrelation coefficient to quantify whether the distr...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an ...
This short paper proves inequalities that restrict the magnitudes of the partial correlations in sta...
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressiv...
We propose a rate equation approach to compute two vertex correlations in scale-free growing network...
This paper extends the results of canonical correlation analysis of Anderson [2002. Canonical correl...
In epidemiological settings, we are often faced with numerous short time series, and a parsimonious ...