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dc.contributor.authorZhang, Bohai
dc.contributor.authorSang, Huiyan
dc.contributor.authorHuang, Jianhua Z.
dc.date.accessioned2016-02-25T13:19:41Z
dc.date.available2016-02-25T13:19:41Z
dc.date.issued2014
dc.identifier.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.
dc.identifier.issn1017-0405
dc.identifier.doi10.5705/ss.2013.260w
dc.identifier.urihttp://hdl.handle.net/10754/598377
dc.description.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.
dc.description.sponsorshipThis 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.
dc.publisherStatistica Sinica (Institute of Statistical Science)
dc.titleFull-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets
dc.typeArticle
dc.identifier.journalStatistica Sinica
dc.contributor.institutionTexas A&M University
kaust.grant.numberKUS-CI-016-04


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