Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

Bohai Zhang, Huiyan Sang, Jianhua Z. Huang

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Various 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.
Original languageEnglish (US)
Pages (from-to)99-114
Number of pages16
JournalStatistica Sinica
Issue number1
StatePublished - 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: This 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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