Hierarchical modeling for extreme values observed over space and time Academic Article

abstract

• We propose a hierarchical modeling approach for explaining a collection of spatially referenced time series of extreme values. We assume that the observations follow generalized extreme value (GEV) distributions whose locations and scales are jointly spatially dependent where the dependence is captured using multivariate Markov random field models specified through coregionalization. In addition, there is temporal dependence in the locations. There are various ways to provide appropriate specifications; we consider four choices. The models can be fitted using a Markov Chain Monte Carlo (MCMC) algorithm to enable inference for parameters and to provide spatio-temporal predictions. We fit the models to a set of gridded interpolated precipitation data collected over a 50-year period for the Cape Floristic Region in South Africa, summarizing results for what appears to be the best choice of model. Springer Science+Business Media, LLC 2008.

authors

• Sang, Huiyan

published proceedings

• ENVIRONMENTAL AND ECOLOGICAL STATISTICS

altmetric score

• 3

author list (cited authors)

• Sang, H., & Gelfand, A. E.

citation count

• 176

complete list of authors

• Sang, Huiyan||Gelfand, Alan E

publication date

• September 2009

publisher

• Springer Nature  Publisher

published in

• Environmental and Ecological Statistics  Journal

keywords

• Coregionalization
• Generalized Extreme Value Distribution
• Markov Random Field
• Precipitation Surfaces
• Spatial Random Effects

Digital Object Identifier (DOI)

• 10.1007/s10651-007-0078-0

start page

• 407

end page

• 426

volume

• 16

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1007%2Fs10651-007-0078-0