Spatial regression models for extremes
type: Item
instance of: scholarly article
title: Spatial regression models for extremes
zbMATH Open document ID: 0935.62109
DOI: 10.1023/A:1009931222386
author: Edward Casson
author: Stuart Coles
published in: Extremes
publication date: 2000-03-01T00:00:00Z
review text: The authors propose a methodology of analysis of extremes in meteorological data which combines the well known extreme value approach and recent achievements in nonlinear spatial statistics. This is important because meteorological data are often recorded at a number of spatial locations. The model structure is the following: at each location, conditional on model parameters, a standard extreme value model is assumed to characterize the data-generating process at extreme levels; across locations the unobservable model parameters are themselves a realization of a smooth stochastic spatial process. Thus, a smooth, but possible non-linear, spatial structure is an intrinsic feature of the model. Markov chain Monte Carlo techniques are used for the necessary computations. A brief overview on a point process approach and details of Markov chain Monte Carlo schemes are given. A simulation study is carried out to illustrate the performance and advantages of spatial models. Finally, the model is applied to data generated from a climatological model in order to characterize the hurricane climate of Gulf and Atlantic coasts of the United States.
Mathematics Subject Classification ID: 62M30
Mathematics Subject Classification ID: 60G70
Mathematics Subject Classification ID: 65C40
zbMATH DE Number: 1408671
zbMATH Keywords: extreme values
zbMATH Keywords: tail distributions
zbMATH Keywords: point processes
zbMATH Keywords: spatial processes
zbMATH Keywords: Markov chain Monte Carlo
zbMATH Keywords: hurricanes
MaRDI profile type: Publication
full work available at URL: a:1009931222386
OpenAlex ID: W20320280
description: scientific article; zbMATH DE number 1408671