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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 4
Nonlin. Processes Geophys., 20, 549–561, 2013
https://doi.org/10.5194/npg-20-549-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 20, 549–561, 2013
https://doi.org/10.5194/npg-20-549-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Jul 2013

Research article | 26 Jul 2013

On the identification of Dragon Kings among extreme-valued outliers

M. Riva1,2, S. P. Neuman2, and A. Guadagnini1,2 M. Riva et al.
  • 1Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. Da Vinci 32, 20133 Milan, Italy
  • 2Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ 85721, USA

Abstract. Extreme values of earth, environmental, ecological, physical, biological, financial and other variables often form outliers to heavy tails of empirical frequency distributions. Quite commonly such tails are approximated by stretched exponential, log-normal or power functions. Recently there has been an interest in distinguishing between extreme-valued outliers that belong to the parent population of most data in a sample and those that do not. The first type, called Gray Swans by Nassim Nicholas Taleb (often confused in the literature with Taleb's totally unknowable Black Swans), is drawn from a known distribution of the tails which can thus be extrapolated beyond the range of sampled values. However, the magnitudes and/or space–time locations of unsampled Gray Swans cannot be foretold. The second type of extreme-valued outliers, termed Dragon Kings by Didier Sornette, may in his view be sometimes predicted based on how other data in the sample behave. This intriguing prospect has recently motivated some authors to propose statistical tests capable of identifying Dragon Kings in a given random sample. Here we apply three such tests to log air permeability data measured on the faces of a Berea sandstone block and to synthetic data generated in a manner statistically consistent with these measurements. We interpret the measurements to be, and generate synthetic data that are, samples from α-stable sub-Gaussian random fields subordinated to truncated fractional Gaussian noise (tfGn). All these data have frequency distributions characterized by power-law tails with extreme-valued outliers about the tail edges.

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