Simulating mixture of sub-Gaussian spatial data

نویسندگان
Mousavi, Seyedeh Somayeh ID 1
Mohammadpour, Adel ID 2

1 Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran

2 Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran

doi
10.22060/ajmc.2023.22015.1130
چکیده

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.

کلیدواژه‌ها
Simulation, Spatial Data, Geostatistical Data, SG$\alpha$S random field