Supplementary Material for: Tukey g-and-h Random Fields

Handle URI:
http://hdl.handle.net/10754/624776
Title:
Supplementary Material for: Tukey <i>g</i>-and-<i>h</i> Random Fields
Authors:
Xu, Ganggang; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
<p>We propose a new class of transGaussian random fields named Tukey <i>g</i>-and-<i>h</i> (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. Supplementary materials for this article are available online.</p>
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Ganggang Xu, & Genton, M. G. (2016). Tukey g-and-h Random Fields. Figshare. https://doi.org/10.6084/m9.figshare.3487658
Publisher:
Figshare
Issue Date:
2016
DOI:
10.6084/m9.figshare.3487658
Type:
Dataset
Is Supplement To:
Xu 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.; DOI:10.1080/01621459.2016.1205501; HANDLE:http://hdl.handle.net/10754/622962
Appears in Collections:
Datasets; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorXu, Ganggangen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-06-06T08:33:44Z-
dc.date.available2017-06-06T08:33:44Z-
dc.date.created2016-07-15en
dc.date.issued2016en
dc.identifier.citationGanggang Xu, & Genton, M. G. (2016). Tukey g-and-h Random Fields. Figshare. https://doi.org/10.6084/m9.figshare.3487658en
dc.identifier.doi10.6084/m9.figshare.3487658en
dc.identifier.urihttp://hdl.handle.net/10754/624776-
dc.description.abstract<p>We propose a new class of transGaussian random fields named Tukey <i>g</i>-and-<i>h</i> (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. Supplementary materials for this article are available online.</p>en
dc.format.extent258684 Bytesen
dc.publisherFigshareen
dc.rightsCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectBiotechnologyen
dc.subjectEcologyen
dc.subjectCanceren
dc.subjectInorganic Chemistryen
dc.subjectPlant Biologyen
dc.titleSupplementary Material for: Tukey <i>g</i>-and-<i>h</i> Random Fieldsen
dc.typeDataseten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
kaust.authorGenton, Marc G.en
dc.type.resourcePaperen
dc.relation.isSupplementToXu 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.en
dc.relation.isSupplementToDOI:10.1080/01621459.2016.1205501en
dc.relation.isSupplementToHANDLE:http://hdl.handle.net/10754/622962en
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