Strong, Weak and False Inverse Power Laws

By Richard Perline. 2005

135 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/60ffef4eaa5a034ea4c3c56c6b2acea1785db320

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luís A. Nunes Amaral
2. R. Dean Malmgren
3. Adilson E. Motter
4. Daniel B. Stouffer

In Statistical and Nonlinear Physics

1. Daniel B. Stouffer
2. Luís A. Nunes Amaral
3. Adilson E. Motter
4. Marco Bee
5. Massimo Riccaboni
6. Stefano Schiavo
7. Santo Fortunato
8. Mark Newman

Citations in the corpus

• The size distribution of US cities: Not Pareto, even in the tail (2013, background): “We conclude that the power-law behavior of the upper tail is less robust than previously claimed, due to the limited power of the available statistical tests (Perline, 2005).”
• A Poissonian explanation for heavy tails in e-mail communication (2008, background): “Indeed , there is mounting evidence that some purported power-law distribut ions in complex systems may not be power-laws at all [38, 39, 40].”