%0 Journal Article
%T Simulating mixture of sub-Gaussian spatial data
%J AUT Journal of Mathematics and Computing
%I Amirkabir University of Technology
%Z 2783-2449
%A Mousavi, Seyedeh Somayeh
%A Mohammadpour, Adel
%D 2024
%\ 01/01/2024
%V 5
%N 1
%P 1-9
%! Simulating mixture of sub-Gaussian spatial data
%K Simulation
%K Spatial Data
%K Geostatistical Data
%K SG$\alpha$S random field
%R 10.22060/ajmc.2023.22015.1130
%X 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.
%U https://ajmc.aut.ac.ir/article_5104_9d05e74ba86ad20ba50061a70f9ba2e3.pdf