A hierarchical Bayesian spatio-temporal model for extreme precipitation events

Souparno Ghosh, Bani K. Mallick

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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..
Original languageEnglish (US)
Pages (from-to)192-204
Number of pages13
JournalEnvironmetrics
Volume22
Issue number2
DOIs
StatePublished - Mar 30 2011
Externally publishedYes

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