Spatial Prediction By Using Unilateral Autoregressive Models In Two-Dimensional Space

A Mojiri, Y Waghei, HR Nili Sani… - Journal of Statistical …, 2018 - jss.irstat.ir
Journal of Statistical Sciences, 2018jss.irstat.ir
Prediction of spatial variability is one of the most important issues in the analysis of spatial
data. So predictions are usually made by assuming that the data follow a spatial model. In
General, the spatial models are the spatial autoregressive (SAR), the conditional
autoregressive and the moving average models. In this paper, we estimated parameter of
SAR (2, 1) model by using maximum likelihood and obtained formulas for predicting in SAR
models, including the prediction within the data (interpolation) and outside the data …
Prediction of spatial variability is one of the most important issues in the analysis of spatial data. So predictions are usually made by assuming that the data follow a spatial model. In General, the spatial models are the spatial autoregressive (SAR), the conditional autoregressive and the moving average models. In this paper, we estimated parameter of SAR (2, 1) model by using maximum likelihood and obtained formulas for predicting in SAR models, including the prediction within the data (interpolation) and outside the data (extrapolation). Finally, we evaluate the prediction methods by using image processing data.
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