Add to ORKGWe propose a novel general approach to the problem of kriging georeferenced functional data. We extend some classical results to nonstationary random fields valued in any Hilbert space. The geometric perspective of our approach allows to deal with either unconstrained or constrained data and opens new perspectives to krige manifold valued random fields
Abstract in Undetermined patial data sets are analysed in many scientific disciplines. Kriging, i.e....
Abstract in Undeterminedpatial data sets are analysed in many scientific disciplines. Kriging, i.e. ...
In various scientific fields properties are represented by functions varying over space. In this pap...
We propose a novel general approach to the problem of kriging georeferenced functional data. We exte...
We propose a novel general approach to the problem of kriging georeferenced functional data. We exte...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...
In an increasing number of studies, collected data are curves; when functional data are spatially de...
In an increasing number of studies, collected data are curves; when functional data are spatially de...
In an increasing number of studies, collected data are curves; when functional data are spatially de...
When dealing with high-dimensional georeferenced data, the need of spatial predictions often results...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibi...
Abstract in Undetermined patial data sets are analysed in many scientific disciplines. Kriging, i.e....
Abstract in Undeterminedpatial data sets are analysed in many scientific disciplines. Kriging, i.e. ...
In various scientific fields properties are represented by functions varying over space. In this pap...
We propose a novel general approach to the problem of kriging georeferenced functional data. We exte...
We propose a novel general approach to the problem of kriging georeferenced functional data. We exte...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
Recently, some specific random fields have been defined based on multivariate distributions. This pa...
In an increasing number of studies, collected data are curves; when functional data are spatially de...
In an increasing number of studies, collected data are curves; when functional data are spatially de...
In an increasing number of studies, collected data are curves; when functional data are spatially de...
When dealing with high-dimensional georeferenced data, the need of spatial predictions often results...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly importan...
In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibi...
Abstract in Undetermined patial data sets are analysed in many scientific disciplines. Kriging, i.e....
Abstract in Undeterminedpatial data sets are analysed in many scientific disciplines. Kriging, i.e. ...
In various scientific fields properties are represented by functions varying over space. In this pap...