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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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Nonlin. Processes Geophys., 24, 737-744, 2017
https://doi.org/10.5194/npg-24-737-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
06 Dec 2017
Optimal heavy tail estimation – Part 1: Order selection
Manfred Mudelsee1,2 and Miguel A. Bermejo1 1Climate Risk Analysis, Heckenbeck, Bad Gandersheim, Germany
2Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Abstract. The tail probability, P, of the distribution of a variable is important for risk analysis of extremes. Many variables in complex geophysical systems show heavy tails, where P decreases with the value, x, of a variable as a power law with a characteristic exponent, α. Accurate estimation of α on the basis of data is currently hindered by the problem of the selection of the order, that is, the number of largest x values to utilize for the estimation. This paper presents a new, widely applicable, data-adaptive order selector, which is based on computer simulations and brute force search. It is the first in a set of papers on optimal heavy tail estimation. The new selector outperforms competitors in a Monte Carlo experiment, where simulated data are generated from stable distributions and AR(1) serial dependence. We calculate error bars for the estimated α by means of simulations. We illustrate the method on an artificial time series. We apply it to an observed, hydrological time series from the River Elbe and find an estimated characteristic exponent of 1.48 ± 0.13. This result indicates finite mean but infinite variance of the statistical distribution of river runoff.

Citation: Mudelsee, M. and Bermejo, M. A.: Optimal heavy tail estimation – Part 1: Order selection, Nonlin. Processes Geophys., 24, 737-744, https://doi.org/10.5194/npg-24-737-2017, 2017.
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Short summary
Risk analysis of extremes has high socioeconomic relevance. Of crucial interest is the tail probability, P, of the distribution of a variable, which is the chance of observing a value equal to or greater than a certain threshold value, x. Many variables in geophysical systems (e.g. climate) show heavy tail behaviour, where P may be rather large. In particular, P decreases with x as a power law that is described by a parameter, α. We present an improved method to estimate α on data.
Risk analysis of extremes has high socioeconomic relevance. Of crucial interest is the tail...
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