Modeling of and inference on multivariate data that have been measured in space, such as temperature and pressure, are challenging tasks in environmental sciences, physics and materials science. We give an overview over and some background on modeling with crosscovariance models. The R package RandomFields supports the simulation, the parameter estimation and the prediction in particular for the linear model of coregionalization, the multivariate Matérn models, the delay model, and a spectrum of physically motivated vector valued models. An example on weather data is considered, illustrating the use of RandomFields for parameter estimation and prediction
In various environmental studies multivariate spatial–temporal correlated data are involved, hence ...
The construction of valid and flexible cross-covariance functions is a fundamental task for modeling...
Given a vectorial data set in two dimensions, a representation on a complex domain is often convenie...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
Spatial data with several components, such as observations of air temperature and pressure in a ce...
The geostatistical analysis of multivariate spatial data for inference as well as joint predictions ...
In various environmental studies multivariate spatial–temporal correlated data are involved, hence ...
The construction of valid and flexible cross-covariance functions is a fundamental task for modeling...
Given a vectorial data set in two dimensions, a representation on a complex domain is often convenie...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
Modeling of and inference on multivariate data that have been measured in space, such as temperature...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spa...
Spatial data with several components, such as observations of air temperature and pressure in a ce...
The geostatistical analysis of multivariate spatial data for inference as well as joint predictions ...
In various environmental studies multivariate spatial–temporal correlated data are involved, hence ...
The construction of valid and flexible cross-covariance functions is a fundamental task for modeling...
Given a vectorial data set in two dimensions, a representation on a complex domain is often convenie...