Robust Estimations for the Tail Index of Weibull-Type Distribution
School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
Department of Actuarial Science, University of Lausanne, Chamberonne, 1015 Lausanne, Switzerland
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 1 September 2018 / Revised: 30 September 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
Based on suitable left-truncated or censored data, two flexible classes of M
-estimations of Weibull tail coefficient are proposed with two additional parameters bounding the impact of extreme contamination. Asymptotic normality with
-rate of convergence is obtained. Its robustness is discussed via its asymptotic relative efficiency and influence function. It is further demonstrated by a small scale of simulations and an empirical study on CRIX.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Gong, C.; Ling, C. Robust Estimations for the Tail Index of Weibull-Type Distribution. Risks 2018, 6, 119.
Gong C, Ling C. Robust Estimations for the Tail Index of Weibull-Type Distribution. Risks. 2018; 6(4):119.
Gong, Chengping; Ling, Chengxiu. 2018. "Robust Estimations for the Tail Index of Weibull-Type Distribution." Risks 6, no. 4: 119.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
For more information on the journal statistics, click here
Multiple requests from the same IP address are counted as one view.