A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

Handle URI:
http://hdl.handle.net/10754/550847
Title:
A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets
Authors:
Xu, Ganggang; Liang, Faming; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets 2014 STATISTICA SINICA
Journal:
STATISTICA SINICA
Issue Date:
1-Jan-2015
DOI:
10.5705/ss.2013.085w
Type:
Article
ISSN:
10170405
Additional Links:
http://www3.stat.sinica.edu.tw/statistica/J25N1/J25N14/J25N14.html
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorXu, Ganggangen
dc.contributor.authorLiang, Famingen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-04-28T12:45:29Zen
dc.date.available2015-04-28T12:45:29Zen
dc.date.issued2015-01-01en
dc.identifier.citationA Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets 2014 STATISTICA SINICAen
dc.identifier.issn10170405en
dc.identifier.doi10.5705/ss.2013.085wen
dc.identifier.urihttp://hdl.handle.net/10754/550847en
dc.description.abstractWhen spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.en
dc.relation.urlhttp://www3.stat.sinica.edu.tw/statistica/J25N1/J25N14/J25N14.htmlen
dc.rightsArchived with thanks to STATISTICA SINICAen
dc.subjectAuxiliary Latticeen
dc.subjectBayesian hierarchical spatio-temporal modelen
dc.subjectGaussian Markov random fielden
dc.subjectlarge datasetsen
dc.subjectspatio-temporal krigingen
dc.titleA Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasetsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalSTATISTICA SINICAen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionTexas A & M Universityen
kaust.authorGenton, Marc G.en
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