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dc.contributor.authorLi, Bo
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2015-08-03T11:06:08Z
dc.date.available2015-08-03T11:06:08Z
dc.date.issued2013-06
dc.identifier.issn01621459
dc.identifier.doi10.1080/01621459.2013.787083
dc.identifier.urihttp://hdl.handle.net/10754/562798
dc.description.abstractWe 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.
dc.description.sponsorshipBo 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.
dc.publisherInforma UK Limited
dc.subjectArchimedeanity
dc.subjectAssociativity
dc.subjectAsymptotic normality
dc.subjectMax-stability
dc.subjectSymmetry
dc.subjectTest
dc.titleNonparametric identification of copula structures
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.identifier.journalJournal of the American Statistical Association
dc.contributor.institutionDepartment of Statistics, Purdue University, West Lafayette, IN 47907-2066, United States
kaust.personGenton, Marc G.


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