Abstract
In this chapter we are interested in the asymptotic comparison of a set of semi-parametric minimum-variance reduced-bias tail-index estimators, at optimal levels and for a wide class of models. Again, as in the classical case, there is not any estimator that can always dominate the alternatives, but interesting clear-cut patterns are found. Consequently, and in practice, a suitable choice of a set of tail-index estimators will jointly enable us to better estimate the tail index, the primary parameter of extreme events.
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Acknowledgements
Research partially supported by FCT—Fundação para a Ciência e a Tecnologia, projects PEst-OE/MAT/UI0006/2011 (CEAUL), PEst-OE/MAT/UI0297/2011 (CMA/UNL) and PTDC/FEDER, EXTREMA.
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Caeiro, F., Gomes, M.I. (2013). Asymptotic Comparison at Optimal Levels of Minimum-Variance Reduced-Bias Tail-Index Estimators. In: Lita da Silva, J., Caeiro, F., Natário, I., Braumann, C. (eds) Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34904-1_8
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DOI: https://doi.org/10.1007/978-3-642-34904-1_8
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