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来源类型Article
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DOI10.1371/journal.pone.0170920
Fitting power-laws in empirical data with estimators that work for all exponents.
Altmann EG; Hanel R; Corominas-Murtra B; Liu B; Thurner S
发表日期2017
出处PLOS ONE 12 (2): e0170920
出版年2017
语种英语
摘要Most standard methods based on maximum likelihood (ML) estimates of power-law exponents can only be reliably used to identify exponents smaller than minus one. The argument that power laws are otherwise not normalizable, depends on the underlying sample space the data is drawn from, and is true only for sample spaces that are unbounded from above. Power-laws obtained from bounded sample spaces (as is the case for practically all data related problems) are always free of such limitations and maximum likelihood estimates can be obtained for arbitrary powers without restrictions. Here we first derive the appropriate ML estimator for arbitrary exponents of power-law distributions on bounded discrete sample spaces. We then show that an almost identical estimator also works perfectly for continuous data. We implemented this ML estimator and discuss its performance with previous attempts. We present a general recipe of how to use these estimators and present the associated computer codes.
主题Advanced Systems Analysis (ASA)
URLhttp://pure.iiasa.ac.at/id/eprint/14397/
来源智库International Institute for Applied Systems Analysis (Austria)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/130941
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Altmann EG,Hanel R,Corominas-Murtra B,et al. Fitting power-laws in empirical data with estimators that work for all exponents.. 2017.
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