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Lookup NU author(s): Dr Clement LeeORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The power law is useful in describing count phenomena such as network degrees and word frequencies. With a single parameter, it captures the main feature that the frequencies are linear on the log-log scale. Nevertheless, there have been criticisms of the power law, for example, that a threshold needs to be preselected without its uncertainty quantified, that the power law is simply inadequate, and that subsequent hypothesis tests are required to determine whether the data could have come from the power law. We propose a modeling framework that combines two different generalizations of the power law, namely the generalized Pareto distribution and the Zipf-polylog distribution, to resolve these issues. The proposed mixture distributions are shown to fit the data well and quantify the threshold uncertainty in a natural way. A model selection step embedded in the Bayesian inference algorithm further answers the question whether the power law is adequate.
Author(s): Lee C, Eastoe E, Farrell A
Publication type: Article
Publication status: Published
Journal: Statistica Neerlandica
Year: 2024
Pages: epub ahead of print
Online publication date: 23/07/2024
Acceptance date: 08/07/2024
Date deposited: 23/07/2024
ISSN (print): 0039-0402
ISSN (electronic): 1467-9574
Publisher: Wiley
URL: https://doi.org/10.1111/stan.12355
DOI: 10.1111/stan.12355
Data Access Statement: The data that support the findings of this study are available from The KONECT project. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from http://konect.cc/ with the permission of The KONECT project.
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