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dc.contributor.authorTagle, Felipe
dc.contributor.authorCastruccio, Stefano
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2017-12-28T07:32:10Z
dc.date.available2017-12-28T07:32:10Z
dc.date.issued2017-12-06
dc.identifier.urihttp://hdl.handle.net/10754/626455
dc.description.abstractLarge, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1712.01992v1
dc.relation.urlhttp://arxiv.org/pdf/1712.01992v1
dc.rightsArchived with thanks to arXiv
dc.titleA Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, United States.
dc.identifier.arxividarXiv:1712.01992
kaust.personGenton, Marc G.
kaust.grant.numberOSR-2015-CRG4-2640
dc.versionv1
refterms.dateFOA2018-06-14T05:36:56Z


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