246,253 research outputs found
Power-law distributions in empirical data
Power-law distributions occur in many situations of scientific interest and
have significant consequences for our understanding of natural and man-made
phenomena. Unfortunately, the detection and characterization of power laws is
complicated by the large fluctuations that occur in the tail of the
distribution -- the part of the distribution representing large but rare events
-- and by the difficulty of identifying the range over which power-law behavior
holds. Commonly used methods for analyzing power-law data, such as
least-squares fitting, can produce substantially inaccurate estimates of
parameters for power-law distributions, and even in cases where such methods
return accurate answers they are still unsatisfactory because they give no
indication of whether the data obey a power law at all. Here we present a
principled statistical framework for discerning and quantifying power-law
behavior in empirical data. Our approach combines maximum-likelihood fitting
methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic
and likelihood ratios. We evaluate the effectiveness of the approach with tests
on synthetic data and give critical comparisons to previous approaches. We also
apply the proposed methods to twenty-four real-world data sets from a range of
different disciplines, each of which has been conjectured to follow a power-law
distribution. In some cases we find these conjectures to be consistent with the
data while in others the power law is ruled out.Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at
http://www.santafe.edu/~aaronc/powerlaws
Power-law distributions in binned empirical data
Many man-made and natural phenomena, including the intensity of earthquakes,
population of cities and size of international wars, are believed to follow
power-law distributions. The accurate identification of power-law patterns has
significant consequences for correctly understanding and modeling complex
systems. However, statistical evidence for or against the power-law hypothesis
is complicated by large fluctuations in the empirical distribution's tail, and
these are worsened when information is lost from binning the data. We adapt the
statistically principled framework for testing the power-law hypothesis,
developed by Clauset, Shalizi and Newman, to the case of binned data. This
approach includes maximum-likelihood fitting, a hypothesis test based on the
Kolmogorov--Smirnov goodness-of-fit statistic and likelihood ratio tests for
comparing against alternative explanations. We evaluate the effectiveness of
these methods on synthetic binned data with known structure, quantify the loss
of statistical power due to binning, and apply the methods to twelve real-world
binned data sets with heavy-tailed patterns.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS710 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Fitting and goodness-of-fit test of non-truncated and truncated power-law distributions
Power-law distributions contain precious information about a large variety of
processes in geoscience and elsewhere. Although there are sound theoretical
grounds for these distributions, the empirical evidence in favor of power laws
has been traditionally weak. Recently, Clauset et al. have proposed a
systematic method to find over which range (if any) a certain distribution
behaves as a power law. However, their method has been found to fail, in the
sense that true (simulated) power-law tails are not recognized as such in some
instances, and then the power-law hypothesis is rejected. Moreover, the method
does not work well when extended to power-law distributions with an upper
truncation. We explain in detail a similar but alternative procedure, valid for
truncated as well as for non-truncated power-law distributions, based in
maximum likelihood estimation, the Kolmogorov-Smirnov goodness-of-fit test, and
Monte Carlo simulations. An overview of the main concepts as well as a recipe
for their practical implementation is provided. The performance of our method
is put to test on several empirical data which were previously analyzed with
less systematic approaches. The databases presented here include the half-lives
of the radionuclides, the seismic moment of earthquakes in the whole world and
in Southern California, a proxy for the energy dissipated by tropical cyclones
elsewhere, the area burned by forest fires in Italy, and the waiting times
calculated over different spatial subdivisions of Southern California. We find
the functioning of the method very satisfactory.Comment: 26 pages, 9 figure
Do wealth distributions follow power laws? Evidence from "rich lists"
We use data on wealth of the richest persons taken from the "rich lists"
provided by business magazines like Forbes to verify if upper tails of wealth
distributions follow, as often claimed, a power-law behaviour. The data sets
used cover the world's richest persons over 1996-2012, the richest Americans
over 1988-2012, the richest Chinese over 2006-2012 and the richest Russians
over 2004-2011. Using a recently introduced comprehensive empirical methodology
for detecting power laws, which allows for testing goodness of fit as well as
for comparing the power-law model with rival distributions, we find that a
power-law model is consistent with data only in 35% of the analysed data sets.
Moreover, even if wealth data are consistent with the power-law model, usually
they are also consistent with some rivals like the log-normal or stretched
exponential distributions.Comment: 17 pages, 6 figures, 1 table, 1 appendi
Emergence of power laws with different power-law exponents from reversal quasi-symmetry and Gibrat’s law
To explore the emergence of power laws in social and economic phenomena, the authors discuss the mechanism whereby reversal quasi-symmetry and Gibrat’s law lead to power laws with different powerlaw exponents. Reversal quasi-symmetry is invariance under the exchange of variables in the joint PDF (probability density function). Gibrat’s law means that the conditional PDF of the exchange rate of variables does not depend on the initial value. By employing empirical worldwide data for firm size, from categories such as plant assets K, the number of employees L, and sales Y in the same year, reversal quasi-symmetry, Gibrat’s laws, and power-law distributions were observed. We note that relations between power-law exponents and the parameter of reversal quasi-symmetry in the same year were first confirmed. Reversal quasi-symmetry not only of two variables but also of three variables was considered. The authors claim the following. There is a plane in 3-dimensional space (log K, log L, log Y ) with respect to which the joint PDF PJ (K,L, Y ) is invariant under the exchange of variables. The plane accurately fits empirical data (K,L, Y ) that follow power-law distributions. This plane is known as the Cobb-Douglas production function, Y = AKαLβ which is frequently hypothesized in economics.
Scaling behavior in land markets
In this paper we present an analysis of power law statistics on land markets.
There have been no other studies that have analyzed power law statistics on
land markets up to now. We analyzed a database of the assessed value of land,
which is officially monitored and made available to the public by the Ministry
of Land, Infrastructure, and Transport Government of Japan. This is the largest
database of Japan's land prices, and consists of approximately 30,000 points
for each year of a 6-year period (1995-2000). By analyzing the data on the
assessed value of land, we were able to determine the power law distributions
of the land prices and of the relative prices of the land. The data fits to a
very good degree the approximation of power law distributions. We also found
that the price fluctuations were amplified with the level of the price. These
results hold for the data for each of the 6 annual intervals. Our empirical
findings present the conditions that any empirically accurate theories of land
market must satisfy.Comment: 12 pages, 5 figure
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