Graphical Methods For Detecting Dependence

Contenido principal del artículo

Autores

Julieth V. Guarín-Escudero
Mario C. Jaramillo-Elorza
Carlos M. Lopera-Gómez

Resumen

Copulas have become a useful tool for modeling data when the dependence among random variables exists and the multivariate normality assumption is not fulfilled. The copulas have been applied in several fields. In finance, copulas are used in asset modeling and risk management. In biomedical studies, copulas are used to model correlated lifetimes and competitive risks [1]. In engineering, copulas are used in multivariate process control and hydrological modeling [2]. The interest in modeling multivariate problems involving dependent variables is generalized in several areas, making this methodology in a convenient way to model the dependence structure of random variables. However, in practice there is not a standard method for selecting a copula among several possible models, so that the choice of an appropriate copula is one of the greatest challenges facing the researcher. In this paper some graphical methods for detecting dependencies among random variables are discussed. 

Detalles del artículo

Referencias

[1] Escarela, G. and Hernández, A. “Modelado de parejas aleatorias usando cópulas”, Revista Colombiana de Estadística 32(1), 33–58, 2009.

[2] Genest, C. and Favre, A. “Everything you always wanted to know about copula modeling but were afraid to ask”, Journal of Hydrologic Engineering 12(4), 347–368, 2007.

[3] Nelsen, R. An Introduction to Copulas, Springer
Science & Business Media, 2007.

[4] Fisher, N. and Switzer, P. “Chiplots for assessing
dependence”, Biometrika 72(2), 253–265,1985.

[5] Genest, C. and Boies, J. “Detecting dependence with Kendall plots”, The American Statistician 57(4), 275–284, 2003.

[6] Embrechts, P., Lindskog, F. and McNeil, A.
“Modelling dependence with copulas and applications to risk management”, Technical Report, Department of Mathematics, ETH Zürich,2001.

[7] Joe, H. Multivariate models and dependence concepts, Chapman and Hall/CRC, 1997.

[8] Cintas del Río, R. “Teoría de cópulas y control de riesgo financiero”, PhD thesis, Universidad Complutense de Madrid, 2007.

[9] Moreno, D. C. “Método para elegir una cópula Arquimediana óptima”, Master’s thesis, Universidad Nacional de Colombia, 2012.

[10] R Core Team R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015.

Descargas

La descarga de datos todavía no está disponible.