A hierarchical Bayesian spatio-temporal model for extreme precipitation events
Type
ArticleAuthors
Ghosh, SouparnoMallick, Bani K.
KAUST Grant Number
KUS-CI-016-04Date
2011-03-30Online Publication Date
2011-03-30Print Publication Date
2011-03Permanent link to this record
http://hdl.handle.net/10754/597284
Metadata
Show full item recordAbstract
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..Citation
Ghosh S, Mallick BK (2011) A hierarchical Bayesian spatio-temporal model for extreme precipitation events. Environmetrics 22: 192–204. Available: http://dx.doi.org/10.1002/env.1043.Sponsors
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.Publisher
WileyJournal
EnvironmetricsDOI
10.1002/env.1043ae974a485f413a2113503eed53cd6c53
10.1002/env.1043