Title : ( Non-stationary spatial autoregressive modeling for the prediction of lattice data )
Authors: A. Mojiri , Y. Waghei , H. R. Nili-Sani , Gholam Reza Mohtashami Borzadaran ,
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Abstract
Spatial autoregressive models are usually used for stationary lattice random fields with a zero or fixed mean. However, many lattice random fields are non-stationary, because they have a non-fixed mean, a non-fixed covariance function, or both. In non-stationary time series, subtracting a fitted trend and differencing are two methods to reach a stationary model. In this paper, these methods have been generalized for non-stationary spatial lattice data. Then, we provide a spatial prediction for each method. By using a simulation study and real data set, we compare the prediction accuracy of the two methods. The results show that predictions made by the trend estimation method are better than differencing method.
Keywords
, Differencing, Lattice Data, Non-stationary, Prediction, Spatial Autoregressive
@article{paperid:1087800,
author = {A. Mojiri and Y. Waghei and H. R. Nili-Sani and Mohtashami Borzadaran, Gholam Reza},
title = {Non-stationary spatial autoregressive modeling for the prediction of lattice data},
journal = {Communications in Statistics Part B: Simulation and Computation},
year = {2021},
volume = {52},
number = {11},
month = {November},
issn = {0361-0918},
pages = {5714--5726},
numpages = {12},
keywords = {Differencing; Lattice Data; Non-stationary; Prediction; Spatial Autoregressive},
}
%0 Journal Article
%T Non-stationary spatial autoregressive modeling for the prediction of lattice data
%A A. Mojiri
%A Y. Waghei
%A H. R. Nili-Sani
%A Mohtashami Borzadaran, Gholam Reza
%J Communications in Statistics Part B: Simulation and Computation
%@ 0361-0918
%D 2021
