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dc.contributor.authorXu, Ganggang
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2017-03-05T06:13:15Z
dc.date.available2017-03-05T06:13:15Z
dc.date.issued2016-07-15
dc.identifier.citationXu G, Genton MG (2016) Tukey g-and-h Random Fields. Journal of the American Statistical Association: 0–0. Available: http://dx.doi.org/10.1080/01621459.2016.1205501.
dc.identifier.issn0162-1459
dc.identifier.issn1537-274X
dc.identifier.doi10.1080/01621459.2016.1205501
dc.identifier.urihttp://hdl.handle.net/10754/622962
dc.description.abstractWe propose a new class of trans-Gaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States.
dc.publisherInforma UK Limited
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/01621459.2016.1205501
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 15 Jul 2016, available online: http://wwww.tandfonline.com/10.1080/01621459.2016.1205501.
dc.subjectContinuous Rank Probability Score
dc.subjectHeavy tails
dc.subjectKriging
dc.subjectLog-Gaussian random field
dc.subjectNon-Gaussian random field
dc.subjectPIT
dc.subjectProbabilistic prediction
dc.subjectSkewness
dc.subjectSpatial outliers
dc.subjectSpatial statistics
dc.subjectTukey g-and-h distribution
dc.titleTukey g-and-h Random Fields
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalJournal of the American Statistical Association
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mathematical Sciences, Binghamton University, Binghamton, NY 13902, USA
kaust.personGenton, Marc G.
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.3487658
refterms.dateFOA2018-01-15T00:00:00Z
display.relations<b> Is Supplemented By:</b> <br/> <ul><li><i>[Dataset]</i> <br/> Ganggang Xu, & Genton, M. G. (2016). Tukey g-and-h Random Fields. Figshare. https://doi.org/10.6084/m9.figshare.3487658. DOI: <a href="https://doi.org/10.6084/m9.figshare.3487658">10.6084/m9.figshare.3487658</a> HANDLE: <a href="http://hdl.handle.net/10754/624776">10754/624776</a></li></ul>
dc.date.published-online2016-07-15
dc.date.published-print2017-07-03


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