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dc.contributor.authorHazra, Arnab
dc.contributor.authorReich, Brian J.
dc.contributor.authorStaicu, Ana Maria
dc.date.accessioned2019-12-04T06:21:05Z
dc.date.available2019-12-04T06:21:05Z
dc.date.issued2019-10-30
dc.identifier.citationHazra, A., Reich, B. J., & Staicu, A. (2019). A multivariate spatial skew- t process for joint modeling of extreme precipitation indexes. Environmetrics. doi:10.1002/env.2602
dc.identifier.doi10.1002/env.2602
dc.identifier.urihttp://hdl.handle.net/10754/660404
dc.description.abstractTo study trends in extreme precipitation across the United States over the years 1951–2017, we analyze 10 climate indexes that represent extreme precipitation, such as annual maximum of daily precipitation and annual maximum of consecutive five-day average precipitation. We consider the gridded data produced by the CLIMDEX project (http://www.climdex.org/gewocs.html), constructed using daily precipitation data. These indexes exhibit spatial and mutual dependence. In this paper, we propose a multivariate spatial skew-t process for joint modeling of extreme precipitation indexes and discuss its theoretical properties. The model framework allows Bayesian inference while maintaining a computational time that is competitive with common multivariate geostatistical approaches. In a numerical study, we find that the proposed model outperforms several simpler alternatives in terms of various model selection criteria. We apply the proposed model to estimate the average decadal change in the extreme precipitation indexes throughout the United States and find several significant local changes.
dc.description.sponsorshipThe authors thank Raphaël Huser at KAUST for some valuable suggestions. The authors would also like to thank an associate editor and two referees whose suggestions have immensely improved this paper.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1002/env.2602
dc.rightsArchived with thanks to Environmetrics
dc.titleA multivariate spatial skew-t process for joint modeling of extreme precipitation indexes
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalEnvironmetrics
dc.rights.embargodate2020-01-01
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Statistics, North Carolina State University, Raleigh, North Carolina
kaust.personHazra, Arnab
refterms.dateFOA2020-01-01T00:00:00Z
dc.date.published-online2019-10-30
dc.date.published-print2020-05


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