Maximum Likelihood Estimation for Directional Conditionally Autoregressive Models
نویسندگان
چکیده
A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed using only the inter-distances between the regions. To accommodate the effect of directions, a new class of spatial models is developed using different weights given to neighbors in different directions. The proposed model generalizes the usual CAR model by accounting for spatial anisotropy. Maximum likelihood estimators are derived and shown to be consistent under some regularity conditions. Simulation studies are presented to evaluate the finite sample performance of the new model as compared to CAR model. Finally the method is illustrated using a data set on the crime rates of Columbus, OH.
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