Separable space–time covariance models are often used for modeling in environmental sciences because of their computational benefits. Unfortunately, there are few formal statistical tests for separability. We adapt a likelihood ratio test based on multivariate repeated measures to the spatio–temporal context. We apply this test to an environmental monitoring data set. Copyright # 2005 John Wiley & Sons, Ltd. key words: elevated CO2; FACE; Kronecker product; separable space–time covarianc
Separable spatio-temporal covariance models, defined as the product of purely spatial and purely tem...
Spectral methods are powerful tools to study and model the dependency structure of spatial temporal ...
In the last two decades space-time models have been studied with increasing interest. The main reaso...
AbstractWe propose a formal test of separability of covariance models based on a likelihood ratio st...
The selection of an appropriate spatio-temporal covariance model for the data under study depends on...
The estimation of covariance operators of spatio-temporal data is in many applications only computat...
Non-separable models are receiving a lot of attention, since they are more flexible to handle empir...
Non-separable models are receiving a lot of attention, since they are more flexible to handle empiri...
There is an increasing wealth of multivariate spatial and multivariate spatio-temporal data appearin...
The assumption of separability is a simplifying and very popular assumption in the analysis of spat...
Testing for separability of space-time covariance functions is of great interest in the analysis of ...
In statistical space-time modeling, the use of non-separable covariance functions is often more rea...
Modelisation and prediction of environmental phenomena, which typically show dependence in space and...
Longitudinal imaging studies have moved to the forefront of medical research due to their ability to...
Longitudinal imaging studies have moved to the forefront of medical research due to their ability to...
Separable spatio-temporal covariance models, defined as the product of purely spatial and purely tem...
Spectral methods are powerful tools to study and model the dependency structure of spatial temporal ...
In the last two decades space-time models have been studied with increasing interest. The main reaso...
AbstractWe propose a formal test of separability of covariance models based on a likelihood ratio st...
The selection of an appropriate spatio-temporal covariance model for the data under study depends on...
The estimation of covariance operators of spatio-temporal data is in many applications only computat...
Non-separable models are receiving a lot of attention, since they are more flexible to handle empir...
Non-separable models are receiving a lot of attention, since they are more flexible to handle empiri...
There is an increasing wealth of multivariate spatial and multivariate spatio-temporal data appearin...
The assumption of separability is a simplifying and very popular assumption in the analysis of spat...
Testing for separability of space-time covariance functions is of great interest in the analysis of ...
In statistical space-time modeling, the use of non-separable covariance functions is often more rea...
Modelisation and prediction of environmental phenomena, which typically show dependence in space and...
Longitudinal imaging studies have moved to the forefront of medical research due to their ability to...
Longitudinal imaging studies have moved to the forefront of medical research due to their ability to...
Separable spatio-temporal covariance models, defined as the product of purely spatial and purely tem...
Spectral methods are powerful tools to study and model the dependency structure of spatial temporal ...
In the last two decades space-time models have been studied with increasing interest. The main reaso...