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| Content Provider | Springer Nature Link |
|---|---|
| Author | Sang, Huiyan Gelfand, Alan E. |
| Copyright Year | 2009 |
| Abstract | We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e.g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. |
| Starting Page | 49 |
| Ending Page | 65 |
| Page Count | 17 |
| File Format | |
| ISSN | 10857117 |
| Journal | Journal of Agricultural, Biological, and Environmental Statistics |
| Volume Number | 15 |
| Issue Number | 1 |
| e-ISSN | 15372693 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2010-01-28 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Copula Gaussian process models Generalized extreme value distribution Maximum precipitation surfaces Spatial interpolation Biostatistics Environmental Monitoring/Analysis Agriculture Statistics for Life Sciences, Medicine, Health Sciences |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty |
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