Noise Convolution Models: Fluids in Stochastic Motion, Non-Gaussian Tempo-Spatial Fields, and a Notion of Tilting
The primary topic of this thesis is a class of tempo-spatial models which are rather flexible in a distributional sense. They prove quite successful in modeling (temporal) dependence structures and go beyond the limitation of Gaussian models, thus allowing for heavy tails and skewness. By generalizing the construction of the above class of models, it is possible to ‘control’ some random geometric