A conditional spatial autoregression (CAR) specifies dependence via a weight matrix. Employing a doubly stochastic weight matrix allows users to interpret the CAR prediction rule as a semiparametric prediction rule and as BLUP with smoothing in addition to other benefits. We examine standard and doubly stochastic weight matrices in the context of an illustrative data set to demonstrate feasibil...
