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    Non-Stationary Dependence Structures for Spatial Extremes

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    Name:
    Huser-Genton_2016_JABES_accepted.pdf
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    4.327Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Huser, Raphaël cc
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2016-03-03
    Online Publication Date
    2016-03-03
    Print Publication Date
    2016-09
    Permanent link to this record
    http://hdl.handle.net/10754/611774
    
    Metadata
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    Abstract
    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 Statistics
    Publisher
    Springer Nature
    Journal
    Journal of Agricultural, Biological, and Environmental Statistics
    DOI
    10.1007/s13253-016-0247-4
    arXiv
    1411.3174
    Additional Links
    http://link.springer.com/10.1007/s13253-016-0247-4
    ae974a485f413a2113503eed53cd6c53
    10.1007/s13253-016-0247-4
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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