Semiparametric tail-index estimation for randomly right-truncated heavy-tailed data

Autor: Mancer, Saida, Necir, Abdelhakim, Benchaira, Souad

Publikováno v: Arab Journal of Mathematical Sciences, 2022, Vol. 30, Issue 2, pp. 171-196.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/AJMS-02-2022-0033

Semiparametric tail-index estimation for randomly right-truncated heavy-tailed data

Autor: Mancer, Saida, Necir, Abdelhakim, Benchaira, Souad

It was shown that when one disposes of a parametric information of the truncation distribution, the semiparametric estimator of the distribution function for truncated data (Wang, 1989) is more efficient than the nonparametric one. On the basis of th

Externí odkaz: http://arxiv.org/abs/2106.01004

Tail Index Estimation for Dependent Data

Autor: Resnick, Sidney, Stărică, Catalin

Publikováno v: The Annals of Applied Probability, 1998 Nov 01. 8(4), 1156-1183.

Externí odkaz: https://www.jstor.org/stable/2667176

Empirical tail copulas for functional data

Autor: John H. J. Einmahl, Johan Segers

Publikováno v: Annals of Statistics, Vol. 49, no. 5, p. 2672-2696 (2021)
The Annals of Statistics, 49(5), 2672-2696. Institute of Mathematical Statistics

For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the dependence in the upper tail. The empirical versions of these function

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4ad7bea516d3d4ec7da8dd35907e8a1
https://doi.org/10.1214/21-aos2050

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