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    Conditional Normal Extreme-Value Copulas

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    Type
    Preprint
    Authors
    Krupskii, Pavel
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2020-06-21
    Permanent link to this record
    http://hdl.handle.net/10754/663908
    
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    Abstract
    We propose a new class of extreme-value copulas which are extreme-value limits of conditional normal models. Conditional normal models are generalizations of conditional independence models, where the dependence among observed variables is modeled using one unobserved factor. Conditional on this factor, the distribution of these variables is given by the Gaussian copula. This structure allows one to build flexible and parsimonious models for data with complex dependence structures, such as data with spatial or temporal dependence. We study the extreme-value limits of these models and show some interesting special cases of the proposed class of copulas. We develop estimation methods for the proposed models and conduct a simulation study to assess the performance of these algorithms. Finally, we apply these copula models to analyze data on monthly wind maxima and stock return minima.
    Sponsors
    This research was supported by the Andrew Sisson fund and KAUST.
    Publisher
    arXiv
    arXiv
    2006.11759
    Additional Links
    https://arxiv.org/pdf/2006.11759
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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