NORTHSTAR: A Parameter Estimation Method for the Spatial Autoregression Model
Date of Submission:
February 9, 2007
Parameter estimation method for the spatial autoregression model (SAR) is important because of the many application domains, such as regional economics, ecology, environmental management, public safety, transportation, public health, business, travel and tourism. However, it is computationally very expensive because of the need to compute the determinant of a large matrix due to Maximum Likelihood Theory. The limitation of previous studies is the need for numerous computations of the computationally expensive determinant term of the likelihood function. In this paper, we present a faster, scalable and NOvel pRediction and estimation TecHnique for the exact SpaTial Auto Regression model solution (NORTHSTAR). We provide a proof of the correctness of this algorithm by showing the objective function to be unimodular. Analytical and experimental results show that the NORTHSTAR algorithm is computationally faster than the related approaches, because it reduces the number of evaluations of the determinant term in the likelihood function.