Add to ORKGEnvironmental data present nearly always a multivariate spatiotemporal structure. Simultaneous diagonalization of the sample matrix variogram is often convenient for isolating space and time correlation of complex latent components. This is also useful for modeling and prediction purposes
none4noIn environmental sciences, it is very common to observe spatio- temporal multiple data concer...
The near simultaneous diagonalization of the sample space-time matrix covariances or variograms make...
Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in...
An environmental data set often concerns different correlated variables measured at some locations o...
In environmental sciences, it is very common to observe spatio-temporal multiple data concerning sev...
In many environmental sciences, several correlated variables are observed at some locations of the d...
In multivariate Geostatistics, the linear coregionalization model (LCM) has been widely used over th...
The product covariance model, the product-sum covariance model, and the integrated product and integ...
Abstract: The near simultaneous diagonalization of the sample space-time matrix covariances or vari...
In multivariate spatio-temporal Geostatistics, direct and cross-correlations among the variables of ...
Modeling and prediction multivariate geostatistical techniques can be successfully applied to study ...
Modeling and prediction multivariate geostatistical techniques can be successfully applied to study ...
In many environmental sciences, the available information concern several correlated variables obse...
The product covariance model, the product–sum covariance model, and the integrated product and integ...
Although there are multiple methods for modeling matrix covariance functions and matrix variograms i...
none4noIn environmental sciences, it is very common to observe spatio- temporal multiple data concer...
The near simultaneous diagonalization of the sample space-time matrix covariances or variograms make...
Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in...
An environmental data set often concerns different correlated variables measured at some locations o...
In environmental sciences, it is very common to observe spatio-temporal multiple data concerning sev...
In many environmental sciences, several correlated variables are observed at some locations of the d...
In multivariate Geostatistics, the linear coregionalization model (LCM) has been widely used over th...
The product covariance model, the product-sum covariance model, and the integrated product and integ...
Abstract: The near simultaneous diagonalization of the sample space-time matrix covariances or vari...
In multivariate spatio-temporal Geostatistics, direct and cross-correlations among the variables of ...
Modeling and prediction multivariate geostatistical techniques can be successfully applied to study ...
Modeling and prediction multivariate geostatistical techniques can be successfully applied to study ...
In many environmental sciences, the available information concern several correlated variables obse...
The product covariance model, the product–sum covariance model, and the integrated product and integ...
Although there are multiple methods for modeling matrix covariance functions and matrix variograms i...
none4noIn environmental sciences, it is very common to observe spatio- temporal multiple data concer...
The near simultaneous diagonalization of the sample space-time matrix covariances or variograms make...
Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in...