6533b823fe1ef96bd127f476
RESEARCH PRODUCT
Model comparison and selection for stationary space–time models
F. MartinezHsin-cheng HuangFrancisco MontesJorge Mateusubject
Statistics and ProbabilityMathematical optimizationCovariance functionbusiness.industryApplied MathematicsModel selectionMultilevel modelKalman filterCovarianceMachine learningcomputer.software_genreComputational MathematicsComputational Theory and MathematicsGoodness of fitBayesian information criterionArtificial intelligenceAkaike information criterionbusinesscomputerMathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-05-01 | Computational Statistics & Data Analysis |