Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets
KAUST Grant NumberKUS-CI-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/598377
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AbstractVarious continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.
CitationZhang B, Sang H, Huang JZ (2014) Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets. STAT SINICA. Available: http://dx.doi.org/10.5705/ss.2013.260w.
SponsorsThis work was partially supported by NSF grant DMS-1007618, NSF grant EARS-1343155, and Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST). Huang's work was also partially supported by NSF grant DMS-1208952.