Spatio-temporal prediction of snow water equivalent using the Kalman filter
Computational Statistics & Data Analysis
Spatio-temporal stationary covariance models
Journal of Multivariate Analysis
Linear combinations of space-time covariance functions and variograms
IEEE Transactions on Signal Processing
Predicting spatio-temporal random fields: Some computational aspects
Computers & Geosciences
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An intensive simulation study to compare the spatio-temporal prediction performances among various space-time models is presented. The models having separable spatio-temporal covariance functions and nonseparable ones, under various scenarios, are also considered. The computational performance among the various selected models are compared. The issue of how to select an appropriate space-time model by accounting for the tradeoff between goodness-of-fit and model complexity is addressed. Performances of the two commonly used model-selection criteria, Akaike information criterion and Bayesian information criterion are examined. Furthermore, a practical application based on the statistical analysis of surface shortwave radiation budget (SSRB) data is presented.