A new class of bivariate copulas: dependence measures and properties

Hakim Bekrizadeh () and Babak Jamshidi ()
Additional contact information
Hakim Bekrizadeh: Payam-e-Noor University
Babak Jamshidi: Shahid Chamran University

METRON, 2017, vol. 75, issue 1, No 3, 50 pages

Abstract: Abstract In this paper, we propose a new class of bivariate Farlie–Gumbel–Morgenstern (FGM) copula. This class includes some known extensions of FGM copulas. Some general formulas for well-known association measures of this class are obtained, and various properties of the proposed model are studied. The tail dependence range of the new class is 0 to 1, and its correlation range is more efficient. We apply some sub-families of the proposed new class to model a dataset of medical science to show the superiority of our approach in comparison with the presented generalized FGM family in the literature. We also present a method to simulate from our generalized FGM copula, and validate our method and its accuracy using the simulation results to recover the same dependency structure of the original data.

Keywords: FGM Copula; Measures of dependence; Medical data; Primary 62H05; Secondary 62H20 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s40300-017-0107-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metron:v:75:y:2017:i:1:d:10.1007_s40300-017-0107-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40300

DOI: 10.1007/s40300-017-0107-1

Access Statistics for this article

METRON is currently edited by Marco Alfo'

More articles in METRON from Springer, Sapienza Università di Roma
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().