Bias-reduced estimators of the Weibull tail-coefficient
By Jean Diebolt, Laurent Gardes, Stéphane Girard, Armelle Guillou. 2008
65 citations recorded by the source corpus.
Abstract In this paper, we consider the problem of the estimation of the Weibull tail-coefficient θ. In particular, we propose a regression model, from which we derive a bias-reduced estimator of θ. This estimator is based on a least-squares approach. The asymptotic normality of this estimator is established. We also introduce an adaptive selection procedure to determine the number of upper order statistics to be used in the estimator. A simulation study as well as an application to a real data set are provided in order to prove the efficiency of the above mentioned methods.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3e4e6fc069e65a1a091e5a30207e8aa72010fd49
How researchers see this paper
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In Finance
- Stéphane Girard
- Laurent Gardes
- Armelle Guillou
- Gilles Stupfler
- Antoine Schorgen
- Yuri Goegebeur
- Jan Beirlant
- Abdelaati Daouia
In Statistics and Probability
- Stéphane Girard
- Laurent Gardes
- Armelle Guillou
- Gilles Stupfler
- Evarist Gin'e
- Antoine Schorgen
- Yuri Goegebeur
- Richard Nickl
Citations in the corpus
- Weibull tail-distributions revisited: a new look at some tail estimators (2011, background): “To this end, an exponential regression model for these tail distributions extending [5, 11, 12, 18] would be of interest.”
- A moving window approach for nonparametric estimation of the conditional tail index (2008, result): “Similar results can be found in [14] for rescaled log-spacings of Weibull-type random variables, and in [3] in the case of Pareto-type random variables without covariate.”
- Estimation of the Weibull tail-coefficient with linear combination of upper order statistics (2008, unknown): “∗Corresponding author”