In this article, autoregressive spatial regression and second-order moving average will be presented to model the outputs of a heavy-tailed skewed spatial random field resulting from the developed multivariate generalized Skew-Laplace distribution. The model parameters are estimated by the maximum likelihood method using the Kolbeck-Leibler divergence criterion. Also, the best spatial predictor will be provided. Then, a simulation study is conducted to validate and evaluate the performance of the proposed model. The method is applied to analyze a real data.
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Saber M M, Mohammadzadeh M. Autoregressive Spatial Regression Model and Second-Order Moving Average for Generalized Skew-Laplace Random Field. JSS 2025; 18 (2) URL: http://jss.irstat.ir/article-1-895-en.html