A hierarchical Bayesian spatio-temporal model for extreme precipitation events Academic Article

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..

authors

• Mallick, Bani

published proceedings

• ENVIRONMETRICS

author list (cited authors)

• Ghosh, S., & Mallick, B. K.

citation count

• 38

complete list of authors

• Ghosh, Souparno||Mallick, Bani K

publication date

• January 2011

publisher

• Wiley  Publisher

published in

• Environmetrics  Journal

keywords

• Copula
• Markov Chain Monte Carlo
• Spatio-temporal

Digital Object Identifier (DOI)

• 10.1002/env.1043

start page

• 192

end page

• 204

volume

• 22

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1002%2Fenv.1043