Abstract
An unbiased and computationally efficient modified least squares (LS) estimation method for identifying parameters of two-dimensional noncausal simultaneous autoregressive models is presented. Some intuitive and mathematical proof of the unbiasedness of the method are given, and a recursive in-order fast algorithm to implement it is introduced. Computer simulation results are given to sustain the theoretical analysis. Both the theoretical analysis and the computer simulation show that the method possesses much higher estimation accuracy and lower computational complexity than the conventional LS estimation method. Compared to the approximate maximum-likelihood method of Kashyap and Chellappa (1983), the scheme is much faster, has the same estimation accuracy, and is parallelizable. >
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