A hierarchical Bayesian spatio-temporal model for extreme precipitation events

Handle URI:
http://hdl.handle.net/10754/597284
Title:
A hierarchical Bayesian spatio-temporal model for extreme precipitation events
Authors:
Ghosh, Souparno; Mallick, Bani K.
Abstract:
We propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. © 2010 John Wiley & Sons, Ltd..
Citation:
Ghosh S, Mallick BK (2011) A hierarchical Bayesian spatio-temporal model for extreme precipitation events. Environmetrics 22: 192–204. Available: http://dx.doi.org/10.1002/env.1043.
Publisher:
Wiley-Blackwell
Journal:
Environmetrics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Mar-2011
DOI:
10.1002/env.1043
Type:
Article
ISSN:
1180-4009
Sponsors:
The first author's research was partially supported by National Science foundation CMG reserach grants DMS-0724704, ATM-0620624, and by Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST). The second author's research was supported by National Science foundation CMG reserach grants ATM-0620624. They gratefully acknowledge two referees for their constructive suggestions that led to significant improvement of the paper.
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Full metadata record

DC FieldValue Language
dc.contributor.authorGhosh, Souparnoen
dc.contributor.authorMallick, Bani K.en
dc.date.accessioned2016-02-25T12:29:48Zen
dc.date.available2016-02-25T12:29:48Zen
dc.date.issued2011-03en
dc.identifier.citationGhosh S, Mallick BK (2011) A hierarchical Bayesian spatio-temporal model for extreme precipitation events. Environmetrics 22: 192–204. Available: http://dx.doi.org/10.1002/env.1043.en
dc.identifier.issn1180-4009en
dc.identifier.doi10.1002/env.1043en
dc.identifier.urihttp://hdl.handle.net/10754/597284en
dc.description.abstractWe propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. © 2010 John Wiley & Sons, Ltd..en
dc.description.sponsorshipThe first author's research was partially supported by National Science foundation CMG reserach grants DMS-0724704, ATM-0620624, and by Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST). The second author's research was supported by National Science foundation CMG reserach grants ATM-0620624. They gratefully acknowledge two referees for their constructive suggestions that led to significant improvement of the paper.en
dc.publisherWiley-Blackwellen
dc.subjectCopulaen
dc.subjectMarkov chain Monte Carloen
dc.subjectSpatio-temporalen
dc.titleA hierarchical Bayesian spatio-temporal model for extreme precipitation eventsen
dc.typeArticleen
dc.identifier.journalEnvironmetricsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-CI-016-04en
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