ArticlePloS one
Beyond Zipf's Law: The Lavalette Rank Function and Its Properties.
Article in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Statistical models for identifying frequent hitters in high throughput screening.Scientific reports · 2020Article
- Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis.Biology direct · 2018Article
- Population patterns in World's administrative units.Royal Society open science · 2017Article
- A Universal Rank-Size Law.PloS one · 2016Article
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Authors and funding
5 authors.
Funding
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Abstract
Although Zipf's law is widespread in natural and social data, one often encounters situations where one or both ends of the ranked data deviate from the power-law function. Previously we proposed the Beta rank function to improve the fitting of data which does not follow a perfect Zipf's law. Here we show that when the two parameters in the Beta rank function have the same value, the Lavalette rank function, the probability density function can be derived analytically. We also show both computationally and analytically that Lavalette distribution is approximately equal, though not identical, to the lognormal distribution. We illustrate the utility of Lavalette rank function in several datasets. We also address three analysis issues on the statistical testing of Lavalette fitting function, comparison between Zipf's law and lognormal distribution through Lavalette function, and comparison between lognormal distribution and Lavalette distribution.
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