TY - JOUR
T1 - Nonparametric identification of copula structures
AU - Li, Bo
AU - Genton, Marc G.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Bo Li is Assistant Professor, Department of Statistics, Purdue University, West Lafayette, IN 47907-2066 (E-mail: [email protected]). Marc G. Genton is Professor, CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia (E-mail: [email protected]). 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.
PY - 2013/6
Y1 - 2013/6
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/562798
UR - http://www.tandfonline.com/doi/abs/10.1080/01621459.2013.787083
UR - http://www.scopus.com/inward/record.url?scp=84890040373&partnerID=8YFLogxK
U2 - 10.1080/01621459.2013.787083
DO - 10.1080/01621459.2013.787083
M3 - Article
SN - 0162-1459
VL - 108
SP - 666
EP - 675
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 502
ER -