A comparison of two graphical methods for detecting dependence
1 recurso en línea (páginas 71-88).
| Main Authors: | , , |
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| Format: | Artículo de revista |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2018
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| Subjects: | |
| Online Access: | http://repositorio.uptc.edu.co/handle/001/2155 |
| _version_ | 1801705866039132160 |
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| author | Guarín Escudero, Julieth Veronica Jaramillo Elorza, Mario César Lopera Gómez, Carlos Mario |
| author_facet | Guarín Escudero, Julieth Veronica Jaramillo Elorza, Mario César Lopera Gómez, Carlos Mario |
| author_sort | Guarín Escudero, Julieth Veronica |
| collection | DSpace |
| description | 1 recurso en línea (páginas 71-88). |
| format | Artículo de revista |
| id | repositorio.uptc.edu.co-001-2155 |
| institution | Repositorio Institucional UPTC |
| language | eng |
| publishDate | 2018 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | dspace |
| spelling | repositorio.uptc.edu.co-001-21552021-06-24T03:06:27Z A comparison of two graphical methods for detecting dependence Una comparación de dos métodos gráficos para detectar dependencia Guarín Escudero, Julieth Veronica Jaramillo Elorza, Mario César Lopera Gómez, Carlos Mario Cópulas (Estadística matemática) Dependencia (Estadística) Estadística matemática Probabilidades Copula Graphics Dependence 1 recurso en línea (páginas 71-88). 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 a first step before modeling phenomena through copulas is to assess whether there is dependence among the variables involved. In this paper some graphical methods to detect dependence are discussed and their performance will be evaluated through a simulation study. An application of graphical methods presented to insurance data is illustrated. Las cópulas se han convertido en una herramienta útil para modelar datos cuando existe una dependencia entre las variables aleatorias y el supuesto de normalidad no se cumple. Las cópulas se han aplicado en diversos campos, tales como finanzas, estudios biomédicos y en ingeniería. El interés en modelar problemas multivariados que involucran variables dependientes se generaliza en diversas áreas, haciendo de esta metodología una forma conveniente para modelar la estructura de dependencia entre las variables aleatorias. Sin embargo, en la práctica un primer paso antes de empezar a modelar fenómenos mediante cópulas es evaluar si existe dependencia entre las variables involucradas y en qué grado. En este artículo algunos métodos gráficos para detectar dependencia son discutidos y el desempeño de los mismos se evaluará a través de un estudio de simulación. Se ilustran los métodos gráficos presentados mediante una aplicación a datos de seguros. Bibliografía: página 88. 2018-09-06T20:01:36Z 2018-09-06T20:01:36Z 2018-02-18 Artículo de revista http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Text https://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 Guarín Escudero, J. V., Jaramillo Elorza, M. C. & Lopera Gómez, C. M. (2018). A comparison of two graphical methods for detecting dependence. Ciencia en Desarrollo, 9(1), 71-88. https://doi.org/10.19053/01217488.v9.n1.2018.5490. http://repositorio.uptc.edu.co/handle/001/2155 2462-7658 http://repositorio.uptc.edu.co/handle/001/2155 10.19053/01217488.v9.n1.2018.5490 eng Escarela, G. and Hernández, A. “Modelado de parejas aleatorias usando cópulas”, Revista Colombiana de Estadística 32(1), 33–58, 2009. 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. Nelsen, R. An Introduction to Copulas, Sprin- ger Science & Business Media, 2007. Fisher, N. and Switzer, P. “Chi-plots for asses- sing dependence”, Biometrika 72(2), 253–265, 1985. Genest, C. and Boies, J. “Detecting dependence with Kendall plots”, The American Statistician 57(4), 275–284, 2003. Nguyen, C. C. and Bhatti, M. I. “Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam”. Jour- nal of International Financial Markets, Institu- tions and Money, 22(4), 758–773, 2012. Vandenberghe, S., Verhoest, N. E. C., and De Baets, B. “Fitting bivariate copulas to the dependence structure between storm characteristics: A detailed analysis based on 105 year 10 min rainfall”. Water resources research, 46(1), 2010. Gargouri-Ellouze, E., and Bargaoui, Z. “Investigation with Kendall plots of infiltration index?maximum rainfall intensity relationship for regionalization”. Physics and Chemistry of the Earth, Parts A/B/C, 34(10), 642-653, 2009. Genest, C. and Mackay, R. J. “Copules archimédiennes et familles de lois bidimensionne- lles dont les marges sont données”, Canadian Journal of Statistics 14(2), 145–159, 1986. Evin, G., Favre A.C. and Genest, C. “Comparison of goodness-of-fit tests adapted to copulas”, Geophysical Research Abstracts, 2005. De Matteis, R. “Fitting copulas to data”. Insti- tute of Mathematics of the University of Zürich, 2001. Embrechts, P., Lindskog, F. and McNeil, A. “Modelling dependence with copulas and applications to risk management”, Technical Re- port, Department of Mathematics, ETH Zurich, 2001. Joe, H. Multivariate models and dependence concepts, Chapman and Hall/CRC, 1997. Cintas del Río, R. “Teoría de cópulas y control de riesgo financiero”, PhD thesis, Universidad Complutense de Madrid, 2007. Moreno, D. C. “Método para elegir una cópula Arquimediana óptima”, Master’s thesis, Universidad Nacional de Colombia, 2012. R Core Team R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015. Lopera, C.M., Jaramillo, M.C. and Arcila, L.D. “Selección de un Modelo Cópula para el Ajuste de Datos Bivariados Dependientes”, Dyna 76(158), 253–263, 2009. Ciencia en Desarrollo;Volumen 9, número 1 (Enero-Junio 2018) Copyright (c) 2018 Universidad Pedagógica y Tecnológica de Colombia https://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Atribución-NoComercial 4.0 Internacional (CC BY-NC 4.0) http://purl.org/coar/access_right/c_abf2 application/pdf application/pdf Universidad Pedagógica y Tecnológica de Colombia https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/5490/pdf |
| spellingShingle | Cópulas (Estadística matemática) Dependencia (Estadística) Estadística matemática Probabilidades Copula Graphics Dependence Guarín Escudero, Julieth Veronica Jaramillo Elorza, Mario César Lopera Gómez, Carlos Mario A comparison of two graphical methods for detecting dependence |
| title | A comparison of two graphical methods for detecting dependence |
| title_full | A comparison of two graphical methods for detecting dependence |
| title_fullStr | A comparison of two graphical methods for detecting dependence |
| title_full_unstemmed | A comparison of two graphical methods for detecting dependence |
| title_short | A comparison of two graphical methods for detecting dependence |
| title_sort | comparison of two graphical methods for detecting dependence |
| topic | Cópulas (Estadística matemática) Dependencia (Estadística) Estadística matemática Probabilidades Copula Graphics Dependence |
| url | http://repositorio.uptc.edu.co/handle/001/2155 |
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