A hierarchical Bayesian spatio-temporal model for extreme precipitation events

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...

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Bibliographic Details
Published in:Environmetrics (London, Ont.) Ont.), 2011-03, Vol.22 (2), p.192-204
Main Authors: Ghosh, Souparno, Mallick, Bani K.
Format: Article
Language:English
Subjects:
Bayesian analysis
copula
Extreme values
Hierarchies
Markov chain Monte Carlo
Mathematical models
Precipitation
spatio-temporal
Temporal logic
Time series
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Summary: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. Copyright © 2010 John Wiley & Sons, Ltd.
ISSN:1180-4009
1099-095X
1099-095X
DOI:10.1002/env.1043