Maximum Likelihood Estimation of Power-Law Exponents for Testing Universality in Complex Systems

Víctor Navas-Portella, Álvaro González, Isabel Serra, Eduard Vives, Álvaro Corral

Producció científica: Capítol de llibreCapítolAvaluat per experts


Power-law-type distributions are extensively found when studying the behavior of many complex systems. However, due to limitations in data acquisition, empirical datasets often only cover a narrow range of observations, making it difficult to establish power-law behavior unambiguously. In this work, we present a statistical procedure to merge different datasets, with two different aims. First, we obtain a broader fitting range for the statistics of different experiments or observations of the same system. Second, we establish whether two or more different systems may belong to the same universality class. By means of maximum likelihood estimation, this methodology provides rigorous statistical information to discern whether power-law exponents characterizing different datasets can be considered equal to each other or not. This procedure is applied to the Gutenberg–Richter law for earthquakes and for synthetic earthquakes (acoustic emission events) generated in the laboratory: labquakes (Navas-Portella et al. Phys Rev E 100:062106, 2019).

Idioma originalAnglès
Títol de la publicacióSEMA SIMAI Springer Series
EditorSpringer Science and Business Media Deutschland GmbH
Nombre de pàgines25
Estat de la publicacióPublicada - 2021
Publicat externament

Sèrie de publicacions

NomSEMA SIMAI Springer Series
ISSN (imprès)2199-3041
ISSN (electrònic)2199-305X


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