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dc.contributor.authorCastrillon, Julio
dc.contributor.authorGenton, Marc G.
dc.contributor.authorYokota, Rio
dc.date.accessioned2015-11-22T07:10:17Z
dc.date.available2015-11-22T07:10:17Z
dc.date.issued2015-11-10
dc.identifier.citationMulti-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets 2015 Spatial Statistics
dc.identifier.issn22116753
dc.identifier.doi10.1016/j.spasta.2015.10.006
dc.identifier.urihttp://hdl.handle.net/10754/582461
dc.description.abstractWe develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic parameters of the model are filtered out thus enabling the estimation of the covariance parameters to be decoupled from the deterministic component. Moreover, the multi-level covariance matrix of the contrasts exhibit fast decay that is dependent on the smoothness of the covariance function. Due to the fast decay of the multi-level covariance matrix coefficients only a small set is computed with a level dependent criterion. We demonstrate our approach on problems of up to 512,000 observations with a Matérn covariance function and highly irregular placements of the observations. In addition, these problems are numerically unstable and hard to solve with traditional methods.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S2211675315000834
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 10 November 2015. DOI: 10.1016/j.spasta.2015.10.006
dc.subjectFast Multipole Method
dc.subjectHierarchical basis
dc.subjectHigh performance computing
dc.subjectSparsification of covariance matrices
dc.titleMulti-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets
dc.typeArticle
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalSpatial Statistics
dc.eprint.versionPost-print
dc.contributor.institutionTokyo Institute of Technology Global Scientific and Computing Center, 2-12-1 i7-2 O-okayama Meguro-ku, 152-8550, Tokyo, Japan
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.arxivid1504.00302
kaust.personCastrillon, Julio
kaust.personGenton, Marc G.
refterms.dateFOA2017-11-10T00:00:00Z
dc.date.published-online2015-11-10
dc.date.published-print2016-11


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