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Accepted Manuscript
Type
ArticleAuthors
Huser, Raphaël
Genton, Marc G.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2016-03-03Online Publication Date
2016-03-03Print Publication Date
2016-09Permanent link to this record
http://hdl.handle.net/10754/611774
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Show full item recordAbstract
Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.Citation
Non-Stationary Dependence Structures for Spatial Extremes 2016 Journal of Agricultural, Biological, and Environmental StatisticsPublisher
Springer NaturearXiv
1411.3174Additional Links
http://link.springer.com/10.1007/s13253-016-0247-4ae974a485f413a2113503eed53cd6c53
10.1007/s13253-016-0247-4