A Alegría, E Porcu - Journal of Multivariate Analysis, 2017 - Elsevier
Abstract Space–time covariance modeling under the Lagrangian framework has been especially popular to study atmospheric phenomena in the presence of transport effects …
Modeling the spatial correlation structure of coregionalized data is a frequent task in numerous fields of the natural sciences. Even in the isotropic case, experimental …
We offer a dual view of the dimple problem related to space-time correlation functions in terms of their contours. We find that the dimple property in the Gneiting class of correlations …
Multivariate random fields allow to simultaneously model multiple spatially indexed variables, playing a fundamental role in geophysical, environmental, and climate disciplines …
AM Mosammam - Communications in Statistics-Simulation and …, 2023 - Taylor & Francis
Covariance function plays very important roles in modeling and in prediction of spatial data. For instance, many statistical inferences such as maximum likelihood estimation and best …
In this paper we propose a method called half spectral composite likelihood for the estimation of spatial–temporal covariance functions which involves a spectral approach in …
RM Espejo, R Fernández-Pascual… - … Research and Risk …, 2017 - Springer
Spatial-depth functional regression is applied for the estimation of ocean temperature, with projection onto the eigenvectors of the empirical covariance operator of the functional …