Abstract
In this chapter, we consider a recent class of generalized negative moment estimators of a negative extreme value index, the primary parameter in statistics of extremes. Apart from the usual integer parameter k, related to the number of top order statistics involved in the estimation, these estimators depend on an extra real parameter θ, which makes them highly flexible and possibly second-order unbiased for a large variety of models. In this chapter, we are interested not only on the adaptive choice of the tuning parameters k and θ, but also on an application of these semi-parametric estimators to the analysis of sets of environmental and simulated data.
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Acknowledgements
Research partially supported by EXTREMA, PTDC/MAT/101736/2008, and National Funds through FCT — Fundação para a Ciência e a Tecnologia, projects PEst-OE/MAT/UI0006/2011 (CEAUL) and PEst-OE/MAT/UI0297/2011 (CMA/UNL).
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Gomes, M.I., Henriques-Rodrigues, L., Caeiro, F. (2013). Refined Estimation of a Light Tail: An Application to Environmental Data. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_14
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DOI: https://doi.org/10.1007/978-3-642-35588-2_14
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