Nonparametric estimation of copula functions for dependence modelling

Canadian Journal of Statistics

Volumn 35, Issue 2, 2007, Pages 265-282

  Chen, Song Xi ^a   Huang, Tzee Ming ^b  

^a E005A Lagomarcino Hall   (United States)

^b NATIONAL CHENGCHI UNIVERSITY   (Taiwan)

Author keywords

Boundary bias; Copula models; Dependence; Kernel estimator

Indexed keywords

EID: 34447643612     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: 10.1002/cjs.5550350205     Document Type: Article

References (25)

1. A. Bowman, P. Hall & T. Prvan (1998). Bandwidth selection for the smoothing of distribution functions. Biometrika, 85, 799-808.
2. S. X. Chen & Y. Fan (2006). Estimation of copula-based semiparametric time series models. Journal of Econometrics, 130, 307-335.
3. S. X. Chen & Huang, T. (2005). Bias and variance calculation for a copula kernel estimator. Technical Report 2005-08, Department of Statistics, Iowa State University, Ames.
4. D. G. Clayton (1978). A model for association in bivariate life tables and its applications in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65, 141-151.
5. R. D. Cook & M. E. Johnson (1981). A family of distributions for modelling nonelliptically symmetric multivariate data. Journal of the Royal Statistical Society Series B, 43, 210-218.
6. P. Deheuvels (1979). La fonction de dépendance empirique et ses propriétés. Un test non paramétrique d'indépendance. Académie Royale de Belgique, Bulletin de la Classe des Sciences, 5e Série, 65, 274-292.
7. P. Embrechts, F. Lindskog, & A. McNeil (2003). Modelling dependence with copulas and applications to risk management. In Handbook of Heavy Tailed Distributions in Finance (S. T. Rachev, ed.), Eisevier, Amsterdam, pp. 329-384.
8. J.-D. Fermanian (2005). Goodness-of-fit tests for copulas. Journal of Multivariate Analysis, 95, 119-152.
9. J.-D. Fermanian, D. Radulović & M. H. Wegkamp (2004). Weak convergence of empirical copula processes. Bernoulli, 10, 847-860.
10. J.-D. Fermanian & O. Scaillet (2003). Nonparametric estimation of copulas for time series. Journal of Risk, 5, 25-54.
11. J. P. Fine & H. Jiang (2000). On association in a copula with time transformations. Biometrika, 87, 559-571.
12. C. Genest, K. Ghoudi & L.-P. Rivest (1995). A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika, 82, 543-552.
13. C. Genest & R. J. MacKay (1986). Copules archimédiennes et familles de lois tridimensionnelles dont les marges sont données. The Canadian Journal of Statistics, 14, 145-159.
14. C. Genest, J.-F. Quessy & B. Rémillard (2006). Goodness-of-fit procedures for copula models based on the probability integral transformation. Scandinavian Journal of Statistics, 33, 337-366.
15. C. Genest & L.-P. Rivest (1993). Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88, 1034-1043.
16. I. Gijbels & J. Mielniczuk (1990). Estimating the density of a copula function. Communications in Statistics: Theory and Methods, 19, 445-464.
17. H. Joe (1997). Multivariate Models and Dependence Concepts. Chapman & Hall, London.
18. M. C. Jones (1993). Simple boundary correction for kernel density estimation. Statistics and Computing, 3, 135-146.
19. M. Lejeune & P. Sarda (1992). Smooth estimators of distribution and density functions. Computational Statistics and Data Analysis, 14, 457-471.
20. H. G. Müller & U. Stadtmüller (1999). Multivariate boundary kernels and a continuous least squares principle. Journal of the Royal Statistical Society Series B, 61, 439-458.
21. R. B. Nelsen (1999). An Introduction to Copulas. Springer, New York.
22. D. Oakes (1982). A model for association in bivariate survival data. Journal of the Royal Statistical Society Series B, 44, 414-422.
23. B. Schweizer & A. Sklar (1983). Probabilistic Metric Spaces. North-Holland, Amsterdam.
24. B. W. Silverman (1986). Density Estimation for Statistics and Data Analysis, Chapman & Hall, London.
25. A. Sklar (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de statistique de l'Université de Paris, 8, 229-231.