TY  - GEN
A1  - Burnecki, Krzysztof
A1  - Wylomanska, Agnieszka
A1  - Chechkin, Aleksei V.
T1  - Discriminating between light- and heavy-tailed distributions with limit theorem
T2  - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - In this paper we propose an algorithm to distinguish between light- and heavy-tailed probability laws underlying random datasets. The idea of the algorithm, which is visual and easy to implement, is to check whether the underlying law belongs to the domain of attraction of the Gaussian or non-Gaussian stable distribution by examining its rate of convergence. The method allows to discriminate between stable and various non-stable distributions. The test allows to differentiate between distributions, which appear the same according to standard Kolmogorov-Smirnov test. In particular, it helps to distinguish between stable and Student's t probability laws as well as between the stable and tempered stable, the cases which are considered in the literature as very cumbersome. Finally, we illustrate the procedure on plasma data to identify cases with so-called L-H transition.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 495 
KW  - levy fight
KW  - statistical-analysis
KW  - fractional dynamics
KW  - stochastic-process
KW  - edge turbulence
KW  - scaling laws
KW  - stable laws
KW  - power-law
KW  - convergence
KW  - fluctuations
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-408172
SN  - 1866-8372
IS  - 495
ER  -