Amirkabir University of Technology AUT Journal of Mathematics and Computing 2783-2449 5 1 2024 01 01 Simulating mixture of sub-Gaussian spatial data 1 9 5104 10.22060/ajmc.2023.22015.1130 EN Seyedeh Somayeh Mousavi Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran Adel Mohammadpour Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran 0000-0002-5079-7025 Journal Article 2022 12 15 Spatial datasets may contain extreme values and exhibit heavy tails. So, the Gaussianity assumption for the corresponding random field is not reasonable. A sub-Gaussian $\alpha$-stable (SG$\alpha$S) random field may be more suitable as a model for heavy-tailed spatial data. This paper focuses on geostatistical data and presents an algorithm for simulating SG$\alpha$S random fields. https://ajmc.aut.ac.ir/article_5104_6a6610feab86a1f294dbbf5855c74af9.pdf