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  1. Journal of Agricultural, Biological, and Environmental Statistics
  2. Journal of Agricultural, Biological, and Environmental Statistics : Volume 15
  3. Journal of Agricultural, Biological, and Environmental Statistics : Volume 15, Issue 1, March 2010
  4. Continuous Spatial Process Models for Spatial Extreme Values
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Journal of Agricultural, Biological, and Environmental Statistics : Volume 22
Journal of Agricultural, Biological, and Environmental Statistics : Volume 21
Journal of Agricultural, Biological, and Environmental Statistics : Volume 20
Journal of Agricultural, Biological, and Environmental Statistics : Volume 19
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18
Journal of Agricultural, Biological, and Environmental Statistics : Volume 17
Journal of Agricultural, Biological, and Environmental Statistics : Volume 16
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15, Issue 4, December 2010
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15, Issue 3, September 2010
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15, Issue 2, June 2010
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15, Issue 1, March 2010
Population Estimates From Aerial Photographic Surveys of Naturally and Variably Marked Bowhead Whales
Estimating Uncertainty in the Revised Universal Soil Loss Equation via Bayesian Melding
Bivariate Stochastic Modeling of Functional Response With Natural Mortality
Continuous Spatial Process Models for Spatial Extreme Values
Continuous Time-Varying Kriging for Spatial Prediction of Functional Data: An Environmental Application
Evaluation of the Accuracy of Diagnostic Tests From Repeated Measurements Without a Gold Standard
Analysis of Clustered Binary Data With Unequal Cluster Sizes: A Semiparametric Bayesian Approach
An Integrated Population Model From Constant Effort Bird-Ringing Data
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14
Journal of Agricultural, Biological, and Environmental Statistics : Volume 13
Journal of Agricultural, Biological, and Environmental Statistics : Volume 12
Journal of Agricultural, Biological, and Environmental Statistics : Volume 11
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10
Journal of Agricultural, Biological, and Environmental Statistics : Volume 9
Journal of Agricultural, Biological, and Environmental Statistics : Volume 8
Journal of Agricultural, Biological, and Environmental Statistics : Volume 7
Journal of Agricultural, Biological, and Environmental Statistics : Volume 6

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Continuous Spatial Process Models for Spatial Extreme Values

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 PDF
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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