Multivariate and functional covariates and conditional copulas

Electronic Journal of Statistics

Volumn 6, Issue , 2012, Pages 1273-1306

  Gijbels, Irène ^a   Omelka, Marek ^b   Veraverbeke, Noël ^c  

^a Katholieke Universiteit Leuven   (Belgium)

^b CHARLES UNIVERSITY   (Czech Republic)

^c HASSELT UNIVERSITY   (Belgium)

Author keywords

Asymptotic representation; Empirical copula process; Functional covariates; Multivariate covariates; Random design; Small ball probability; Smoothing

Indexed keywords

EID: 84875400131     PISSN: 19357524     EISSN: None     Source Type: Journal    
DOI: 10.1214/12-EJS712     Document Type: Article

References (19)

1. Abegaz, F., Gijbels, I. and Veraverbeke, N. (2012). Semiparametric estimation of conditional copulas., J. Multivariate Anal. 110 43-73.
2. Acar, E. F., Craiu, R. V. and Yao, F. (2011). Dependence calibration in conditional copulas: a nonparametric approach., Biometrics 67 445-453.
3. Baíllo, A. and Grané, A. (2009). Local linear regression for functional predictor and scalar response., J. Multivariate Anal. 100 102-111.
4. Barrientos-Marin, J., Ferraty, F. and Vieu, P. (2010). Locally modelled regression and functional data., J. Nonparametr. Stat. 22 617-632.
5. Berlinet, A., Elamine, A. and Mas, A. (2011). Local linear regression for functional data., Ann. Inst. Statist. Math. 63 1047-1075.
6. Borggaard, C. and Thodberg, H. H. (1992). Optimal minimal neural interpretation of spectra., Analytical Chemistry 64 545-551.
7. Burba, F., Ferraty, F. and Vieu, P. (2009). k-Nearest Neighbour method in functional nonparametric regression., J. Nonparametr. Stat. 21 453-469.
8. Ferraty, F., Van Keilegom, I. and Vieu, P. (2010). On the Validity of the Bootstrap in Non-Parametric Functional Regression., Scand. J. Statist. 37 286-306.
9. Ferraty, F. and Vieu, P. (2002). The functional nonparametric model and application to spectrometric data., Computation. Stat. 17 545-564.
10. Ferraty, F., Mas, A. and Vieu, P. (2007). Nonparametric regression on functional data: inference and practical aspects., Aust. N. Z. J. Stat. 49 267-286.
11. Ferraty, F. and Vieu, P. (2006)., Nonparametric functional data analysis. Theory and practice. Springer, New York.
12. Gijbels, I., Veraverbeke, N. and Omelka, M. (2011). Conditional copulas, association measures and their application., Comput. Stat. Data An. 55 1919-1932.
13. Hafner, C. M. and Reznikova, O. (2010). Efficient estimation of a semiparametric dynamic copula model., Comput. Stat. Data An. 54 2609-2627.
14. Hall, P., Müller, H. G. and Yao, F. (2009). Estimation of functional derivatives., Ann. Statist. 37 3307-3329.
15. Nelsen, R. B. (2006)., An Introduction to Copulas. Springer, New York Second Edition.
16. Ruppert, D. and Wand, M. P. (1994). Multivariate locally weighted least squares regression., Ann. Statist. 22 1346-1370.
17. Segers, J. (2012). Weak convergence of empirical copula processes under nonrestrictive smoothness assumptions., Bernoulli 18 764-782.
18. van der Vaart, A. W. and Wellner, J. A. (1996)., Weak Convergence and Empirical Processes. Springer, New York.
19. Veraverbeke, N., Omelka, M. and Gijbels, I. (2011). Estimation of a conditional copula and association measures., Scand. J. Statist. 38 766-780.