Multivariate log-skew-elliptical distributions with applications to precipitation data

Handle URI:
http://hdl.handle.net/10754/598922
Title:
Multivariate log-skew-elliptical distributions with applications to precipitation data
Authors:
Marchenko, Yulia V.; Genton, Marc G.
Abstract:
We introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.
Citation:
Marchenko YV, Genton MG (2009) Multivariate log-skew-elliptical distributions with applications to precipitation data. Environmetrics 21: 318–340. Available: http://dx.doi.org/10.1002/env.1004.
Publisher:
Wiley-Blackwell
Journal:
Environmetrics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
13-Jul-2009
DOI:
10.1002/env.1004
Type:
Article
ISSN:
1180-4009; 1099-095X
Sponsors:
Genton’s research was supported in part by NSF grants DMS-0504896, CMG ATM-0620624, and by AwardNo. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thankReinaldo B. Arellano-Valle, Kenneth P. Bowman, and a referee for their helpful comments and suggestions. The mapof US climatic regions was made available by the National Oceanic and Atmospheric Administration/Departmentof Commerce
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMarchenko, Yulia V.en
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-25T13:43:46Zen
dc.date.available2016-02-25T13:43:46Zen
dc.date.issued2009-07-13en
dc.identifier.citationMarchenko YV, Genton MG (2009) Multivariate log-skew-elliptical distributions with applications to precipitation data. Environmetrics 21: 318–340. Available: http://dx.doi.org/10.1002/env.1004.en
dc.identifier.issn1180-4009en
dc.identifier.issn1099-095Xen
dc.identifier.doi10.1002/env.1004en
dc.identifier.urihttp://hdl.handle.net/10754/598922en
dc.description.abstractWe introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.en
dc.description.sponsorshipGenton’s research was supported in part by NSF grants DMS-0504896, CMG ATM-0620624, and by AwardNo. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thankReinaldo B. Arellano-Valle, Kenneth P. Bowman, and a referee for their helpful comments and suggestions. The mapof US climatic regions was made available by the National Oceanic and Atmospheric Administration/Departmentof Commerceen
dc.publisherWiley-Blackwellen
dc.subjectClimatic regionsen
dc.subjectExtreme eventsen
dc.subjectHeavy tailsen
dc.subjectLog-skew-normalen
dc.subjectLog-skew-ten
dc.subjectMomentsen
dc.subjectMultivariateen
dc.subjectPrecipitationen
dc.subjectSkewnessen
dc.titleMultivariate log-skew-elliptical distributions with applications to precipitation dataen
dc.typeArticleen
dc.identifier.journalEnvironmetricsen
dc.contributor.institutionStataCorp, College Station, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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