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    Nonparametric identification of copula structures

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    Type
    Article
    Authors
    Li, Bo
    Genton, Marc G. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2013-06
    Permanent link to this record
    http://hdl.handle.net/10754/562798
    
    Metadata
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    Abstract
    We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process.We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets. © 2013 American Statistical Association.
    Citation
    Li, B., & Genton, M. G. (2013). Nonparametric Identification of Copula Structures. Journal of the American Statistical Association, 108(502), 666–675. doi:10.1080/01621459.2013.787083
    Sponsors
    Bo Li is Assistant Professor, Department of Statistics, Purdue University, West Lafayette, IN 47907-2066 (E-mail: boli@purdue.edu). Marc G. Genton is Professor, CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia (E-mail: marc.genton@kaust.edu.sa). Li's research was partially supported by the National Science Foundation grant DMS-1007686. The authors thank the Editor, an Associate Editor, two anonymous referees, Christian Genest, Ivan Kojadinovic, Johanna Neslehova, Bruno Remillard, and Stanislav Volgushev for their helpful comments and suggestions, as well as Jean-Francois Quessy and Stanislav Volgushev for providing code for their testing procedures.
    Publisher
    Informa UK Limited
    Journal
    Journal of the American Statistical Association
    DOI
    10.1080/01621459.2013.787083
    ae974a485f413a2113503eed53cd6c53
    10.1080/01621459.2013.787083
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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