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    A hierarchical Bayesian spatio-temporal model for extreme precipitation events

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    Type
    Article
    Authors
    Ghosh, Souparno
    Mallick, Bani K.
    KAUST Grant Number
    KUS-CI-016-04
    Date
    2011-03-30
    Online Publication Date
    2011-03-30
    Print Publication Date
    2011-03
    Permanent link to this record
    http://hdl.handle.net/10754/597284
    
    Metadata
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    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.
    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.
    Publisher
    Wiley
    Journal
    Environmetrics
    DOI
    10.1002/env.1043
    ae974a485f413a2113503eed53cd6c53
    10.1002/env.1043
    Scopus Count
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    Publications Acknowledging KAUST Support

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