Nonparametric identification of copula structures

Handle URI:
http://hdl.handle.net/10754/562798
Title:
Nonparametric identification of copula structures
Authors:
Li, Bo; Genton, Marc G. ( 0000-0001-6467-2998 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Spatio-Temporal Statistics and Data Analysis Group
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
Issue Date:
Jun-2013
DOI:
10.1080/01621459.2013.787083
Type:
Article
ISSN:
01621459
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.
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Boen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-08-03T11:06:08Zen
dc.date.available2015-08-03T11:06:08Zen
dc.date.issued2013-06en
dc.identifier.issn01621459en
dc.identifier.doi10.1080/01621459.2013.787083en
dc.identifier.urihttp://hdl.handle.net/10754/562798en
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.en
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.en
dc.publisherInforma UK Limiteden
dc.subjectArchimedeanityen
dc.subjectAssociativityen
dc.subjectAsymptotic normalityen
dc.subjectMax-stabilityen
dc.subjectSymmetryen
dc.subjectTesten
dc.titleNonparametric identification of copula structuresen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Groupen
dc.identifier.journalJournal of the American Statistical Associationen
dc.contributor.institutionDepartment of Statistics, Purdue University, West Lafayette, IN 47907-2066, United Statesen
kaust.authorGenton, Marc G.en
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