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Autoregressive Spatial Regression Model and Second-Order Moving Average for Generalized Skew-Laplace Random Field
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Mohammad Mehdi Saber *    , Mohsen Mohammadzadeh |
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Abstract: (742 Views) |
| 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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| Keywords: Multivariate generalized skew Laplace distribution, Autoregressive and moving average spatial regression model, Kolbeck-Leibler divergence. |
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Full-Text [PDF 401 kb]
(499 Downloads)
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Type of Study: Research |
Subject:
Spatial Statistics
Received: 2024/05/17 | Accepted: 2024/05/30 | Published: 2024/12/2
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| References |
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