Time series: theory and methods
Time series: theory and methods
FORTRAN programs for space-time modeling
Computers & Geosciences
Space-time variograms and a functional form for total air pollution measurements
Computational Statistics & Data Analysis
Spatio-temporal stationary covariance models
Journal of Multivariate Analysis
Model comparison and selection for stationary space-time models
Computational Statistics & Data Analysis
FORTRAN programs for space-time multivariate modeling and prediction
Computers & Geosciences
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Modeling of spatio-temporal processes is critical in many fields such as environmental sciences, meteorology, hydrology and reservoir engineering. Nowadays spatio-temporal analysis cannot be adequately faced without considering important issues, such as: (a) modeling the spatio-temporal random field from which data might be reasonably derived, (b) choosing suitable covariance models which describe the spatio-temporal correlation of the variables of interest, (c) using adequate software packages which tackle different inferential problems. In this paper, the above aspects are properly analyzed. In particular, three different space-time random field decomposition choices are considered and the flexibility of using the generalized product-sum model is highlighted. A customized GSLib routine for kriging in space-time is proposed. This Fortran routine, named ''K2ST'', is based on the use of the generalized product -sum model, with nested structures, and appropriate space-time search neighborhoods. An application to NO"2 pollutant in an urban area is presented. In order to compare kriging results associated with three hypotheses of space-time random field decomposition, correlation coefficients and standardized errors between true values and predicted ones are computed. Moreover, nonparametric tests are applied to check the significance of the difference among the three approaches.