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Nonparametric estimator of the tail dependence coefficient: balancing bias and variance.
- Source :
- Statistical Papers; Oct2024, Vol. 65 Issue 8, p4875-4913, 39p
- Publication Year :
- 2024
-
Abstract
- A theoretical expression is derived for the mean squared error of a nonparametric estimator of the tail dependence coefficient, depending on a threshold that defines which rank delimits the tails of a distribution. We propose a new method to optimally select this threshold. It combines the theoretical mean squared error of the estimator with a parametric estimation of the copula linking observations in the tails. Using simulations, we compare this semiparametric method with other approaches proposed in the literature, including the plateau-finding algorithm. [ABSTRACT FROM AUTHOR]
- Subjects :
- NONPARAMETRIC estimation
CENSORSHIP
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 09325026
- Volume :
- 65
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Statistical Papers
- Publication Type :
- Academic Journal
- Accession number :
- 180153799
- Full Text :
- https://doi.org/10.1007/s00362-024-01582-w