A hierarchical Bayesian spatio-temporal model for extreme precipitation events
Article first published online: 28 APR 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Volume 22, Issue 2, pages 192–204, March 2011
How to Cite
Ghosh, S. and Mallick, B. K. (2011), A hierarchical Bayesian spatio-temporal model for extreme precipitation events. Environmetrics, 22: 192–204. doi: 10.1002/env.1043
- Issue published online: 30 MAR 2011
- Article first published online: 28 APR 2010
- Manuscript Accepted: 4 JAN 2010
- Manuscript Received: 12 AUG 2008
- Markov chain Monte Carlo
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.