Type
ArticleAuthors
Li, BoGenton, Marc G.

KAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Spatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2013-06Permanent link to this record
http://hdl.handle.net/10754/562798
Metadata
Show full item recordAbstract
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:10.1080/01621459.2013.787083Sponsors
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 Limitedae974a485f413a2113503eed53cd6c53
10.1080/01621459.2013.787083