A hierarchical Bayesian spatio-temporal model for extreme precipitation events

Souparno Ghosh, Bani K. Mallick

Research output: Contribution to journalArticlepeer-review

37 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

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

Fingerprint

Dive into the research topics of 'A hierarchical Bayesian spatio-temporal model for extreme precipitation events'. Together they form a unique fingerprint.

Cite this