Nonparametric identification of copula structures

Bo Li, Marc G. Genton

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)666-675
Number of pages10
JournalJournal of the American Statistical Association
Volume108
Issue number502
DOIs
StatePublished - Dec 16 2013

Fingerprint

Nonparametric Identification
Copula
Radial Symmetry
Symmetry
Testing
Associativity
Asymptotic distribution
Simulation Experiment
Sample Size
Evaluate
Nonparametric identification

Keywords

  • Archimedeanity
  • Associativity
  • Asymptotic normality
  • Max-stability
  • Symmetry
  • Test

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Nonparametric identification of copula structures. / Li, Bo; Genton, Marc G.

In: Journal of the American Statistical Association, Vol. 108, No. 502, 16.12.2013, p. 666-675.

Research output: Contribution to journalArticle

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