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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 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.
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 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.
A hierarchical Bayesian spatio‐temporal model for extreme precipitation events
Ghosh, Souparno (author) / Mallick, Bani K. (author)
Environmetrics ; 22 ; 192-204
2011-03-01
13 pages
Article (Journal)
Electronic Resource
English
A hierarchical Bayesian spatio‐temporal model for extreme precipitation events
| Online Contents | 2011