HOME

TheInfoList



Click Here for Items Related To - Scaling law
OR:
Scaling law on:   [Wikipedia]   [Google]   [Amazon]

In statistics, a power law is a
functional relationship In mathematics, a function from a set to a set assigns to each element of exactly one element of .; the words map, mapping, transformation, correspondence, and operator are often used synonymously. The set is called the domain of the func ...
between two quantities, where a
relative change In any quantitative science, the terms relative change and relative difference are used to compare two quantities while taking into account the "sizes" of the things being compared, i.e. dividing by a ''standard'' or ''reference'' or ''starting'' va ...
in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a
power Power most often refers to: * Power (physics), meaning "rate of doing work" ** Engine power, the power put out by an engine ** Electric power * Power (social and political), the ability to influence people or events ** Abusive power Power may ...
of another. For instance, considering the area of a square in terms of the length of its side, if the length is doubled, the area is multiplied by a factor of four.


Empirical examples

The distributions of a wide variety of physical, biological, and man-made phenomena approximately follow a power law over a wide range of magnitudes: these include the sizes of craters on the moon and of solar flares, the foraging pattern of various species, the sizes of activity patterns of neuronal populations, the frequencies of words in most languages, frequencies of
family name In some cultures, a surname, family name, or last name is the portion of one's personal name that indicates one's family, tribe or community. Practices vary by culture. The family name may be placed at either the start of a person's full name, ...
s, the
species richness Species richness is the number of different species represented in an ecological community, landscape or region. Species richness is simply a count of species, and it does not take into account the abundances of the species or their relative a ...
in
clades A clade (), also known as a monophyletic group or natural group, is a group of organisms that are monophyletic – that is, composed of a common ancestor and all its lineal descendants – on a phylogenetic tree. Rather than the English term, t ...
of organisms, the sizes of power outages, volcanic eruptions, human judgments of stimulus intensity and many other quantities. Few empirical distributions fit a power law for all their values, but rather follow a power law in the tail.
Acoustic attenuation Acoustic attenuation is a measure of the energy loss of sound propagation in media. Most media have viscosity and are therefore not ideal media. When sound propagates in such media, there is always thermal consumption of energy caused by viscosity. ...
follows frequency power-laws within wide frequency bands for many complex media. Allometric scaling laws for relationships between biological variables are among the best known power-law functions in nature.


Properties


Scale invariance

One attribute of power laws is their
scale invariance In physics, mathematics and statistics, scale invariance is a feature of objects or laws that do not change if scales of length, energy, or other variables, are multiplied by a common factor, and thus represent a universality. The technical ter ...
. Given a relation f(x) = ax^, scaling the argument x by a constant factor c causes only a proportionate scaling of the function itself. That is, :f(c x) = a(c x)^ = c^ f( x ) \propto f(x),\! where \propto denotes
direct proportionality In mathematics, two sequences of numbers, often experimental data, are proportional or directly proportional if their corresponding elements have a constant ratio, which is called the coefficient of proportionality or proportionality constan ...
. That is, scaling by a constant c simply multiplies the original power-law relation by the constant c^. Thus, it follows that all power laws with a particular scaling exponent are equivalent up to constant factors, since each is simply a scaled version of the others. This behavior is what produces the linear relationship when logarithms are taken of both f(x) and x, and the straight-line on the log–log plot is often called the ''signature'' of a power law. With real data, such straightness is a necessary, but not sufficient, condition for the data following a power-law relation. In fact, there are many ways to generate finite amounts of data that mimic this signature behavior, but, in their asymptotic limit, are not true power laws (e.g., if the generating process of some data follows a
Log-normal distribution In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable is log-normally distributed, then has a normal ...
). Thus, accurately fitting and validating power-law models is an active area of research in statistics; see below.


Lack of well-defined average value

A power-law x^ has a well-defined mean over x \in ,\infty)_only_if__k_>_2_,_and_it_has_a_finite_variance_only_if_k_>3;_most_identified_power_laws_in_nature_have_exponents_such_that_the_mean_is_well-defined_but_the_variance_is_not,_implying_they_are_capable_of_black_swan_theory.html" "title="variance.html" ;"title=",\infty) only if k > 2 , and it has a finite variance">,\infty) only if k > 2 , and it has a finite variance only if k >3; most identified power laws in nature have exponents such that the mean is well-defined but the variance is not, implying they are capable of black swan theory">black swan The black swan (''Cygnus atratus'') is a large waterbird, a species of swan which breeds mainly in the southeast and southwest regions of Australia. Within Australia, the black swan is nomadic, with erratic migration patterns dependent upon cl ...
behavior. This can be seen in the following thought experiment: imagine a room with your friends and estimate the average monthly income in the room. Now imagine the world's richest person entering the room, with a monthly income of about 1 1,000,000,000, billion US$. What happens to the average income in the room? Income is distributed according to a power-law known as the Pareto distribution (for example, the net worth of Americans is distributed according to a power law with an exponent of 2). On the one hand, this makes it incorrect to apply traditional statistics that are based on variance and
standard deviation In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, whil ...
(such as regression analysis). On the other hand, this also allows for cost-efficient interventions. For example, given that car exhaust is distributed according to a power-law among cars (very few cars contribute to most contamination) it would be sufficient to eliminate those very few cars from the road to reduce total exhaust substantially. The median does exist, however: for a power law ''x'' –''k'', with exponent , it takes the value 21/(''k'' – 1)''x''min, where ''x''min is the minimum value for which the power law holds.


Universality

The equivalence of power laws with a particular scaling exponent can have a deeper origin in the dynamical processes that generate the power-law relation. In physics, for example, phase transitions in thermodynamic systems are associated with the emergence of power-law distributions of certain quantities, whose exponents are referred to as the
critical exponent Critical or Critically may refer to: *Critical, or critical but stable, medical states **Critical, or intensive care medicine *Critical juncture, a discontinuous change studied in the social sciences. *Critical Software, a company specializing in ...
s of the system. Diverse systems with the same critical exponents—that is, which display identical scaling behaviour as they approach criticality—can be shown, via renormalization group theory, to share the same fundamental dynamics. For instance, the behavior of water and CO2 at their boiling points fall in the same universality class because they have identical critical exponents. In fact, almost all material phase transitions are described by a small set of universality classes. Similar observations have been made, though not as comprehensively, for various self-organized critical systems, where the critical point of the system is an
attractor In the mathematical field of dynamical systems, an attractor is a set of states toward which a system tends to evolve, for a wide variety of starting conditions of the system. System values that get close enough to the attractor values remain ...
. Formally, this sharing of dynamics is referred to as universality, and systems with precisely the same critical exponents are said to belong to the same universality class.


Power-law functions

Scientific interest in power-law relations stems partly from the ease with which certain general classes of mechanisms generate them. The demonstration of a power-law relation in some data can point to specific kinds of mechanisms that might underlie the natural phenomenon in question, and can indicate a deep connection with other, seemingly unrelated systems; see also universality above. The ubiquity of power-law relations in physics is partly due to dimensional constraints, while in
complex systems A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication sy ...
, power laws are often thought to be signatures of hierarchy or of specific
stochastic processes In probability theory and related fields, a stochastic () or random process is a mathematical object usually defined as a family of random variables. Stochastic processes are widely used as mathematical models of systems and phenomena that appea ...
. A few notable examples of power laws are Pareto's law of income distribution, structural self-similarity of fractals, and scaling laws in biological systems. Research on the origins of power-law relations, and efforts to observe and validate them in the real world, is an active topic of research in many fields of science, including physics, computer science, linguistics, geophysics, neuroscience, systematics, sociology, economics and more. However, much of the recent interest in power laws comes from the study of probability distributions: The distributions of a wide variety of quantities seem to follow the power-law form, at least in their upper tail (large events). The behavior of these large events connects these quantities to the study of theory of large deviations (also called extreme value theory), which considers the frequency of extremely rare events like
stock market crash A stock market crash is a sudden dramatic decline of stock prices across a major cross-section of a stock market, resulting in a significant loss of paper wealth. Crashes are driven by panic selling and underlying economic factors. They often f ...
es and large natural disasters. It is primarily in the study of statistical distributions that the name "power law" is used. In empirical contexts, an approximation to a power-law o(x^k) often includes a deviation term \varepsilon, which can represent uncertainty in the observed values (perhaps measurement or sampling errors) or provide a simple way for observations to deviate from the power-law function (perhaps for stochastic reasons): :y = ax^k + \varepsilon.\! Mathematically, a strict power law cannot be a probability distribution, but a distribution that is a truncated power function is possible: p(x) = C x^ for x > x_\text where the exponent \alpha (Greek letter
alpha Alpha (uppercase , lowercase ; grc, ἄλφα, ''álpha'', or ell, άλφα, álfa) is the first letter of the Greek alphabet. In the system of Greek numerals, it has a value of one. Alpha is derived from the Phoenician letter aleph , whic ...
, not to be confused with scaling factor a used above) is greater than 1 (otherwise the tail has infinite area), the minimum value x_\text is needed otherwise the distribution has infinite area as ''x'' approaches 0, and the constant ''C'' is a scaling factor to ensure that the total area is 1, as required by a probability distribution. More often one uses an asymptotic power law – one that is only true in the limit; see power-law probability distributions below for details. Typically the exponent falls in the range 2 < \alpha < 3, though not always.


Examples

More than a hundred power-law distributions have been identified in physics (e.g. sandpile avalanches), biology (e.g. species extinction and body mass), and the social sciences (e.g. city sizes and income). Among them are:


Astronomy

*
Kepler's third law In astronomy, Kepler's laws of planetary motion, published by Johannes Kepler between 1609 and 1619, describe the orbits of planets around the Sun. The laws modified the heliocentric theory of Nicolaus Copernicus, replacing its circular orbits ...
* The
initial mass function In astronomy, the initial mass function (IMF) is an empirical function that describes the initial distribution of masses for a population of stars. The IMF is an output of the process of star formation. The IMF is often given as a probability di ...
of stars *The differential energy spectrum of
cosmic-ray Cosmic rays are high-energy particles or clusters of particles (primarily represented by protons or atomic nuclei) that move through space at nearly the speed of light. They originate from the Sun, from outside of the Solar System in our own ...
nuclei *The M–sigma relation


Physics

*The
Angstrom exponent The Angstrom exponentGregory L. Schuster, Oleg Dubovik and Brent N. Holben (2006): "Angstrom exponent and bimodal aerosol size distributions". ''Journal of Geophysical Research: Atmospheres'', volume 111, issue D7, article D07207, pages 1-14. Itaru ...
in aerosol optics *The frequency-dependency of
acoustic attenuation Acoustic attenuation is a measure of the energy loss of sound propagation in media. Most media have viscosity and are therefore not ideal media. When sound propagates in such media, there is always thermal consumption of energy caused by viscosity. ...
in complex media *The Stefan–Boltzmann law *The input-voltage–output-current curves of field-effect transistors and
vacuum tubes A vacuum tube, electron tube, valve (British usage), or tube (North America), is a device that controls electric current flow in a high vacuum between electrodes to which an electric potential difference has been applied. The type known as a ...
approximate a square-law relationship, a factor in "
tube sound Tube sound (or valve sound) is the characteristic sound associated with a vacuum tube amplifier (valve amplifier in British English), a vacuum tube-based audio amplifier. At first, the concept of ''tube sound'' did not exist, because practically ...
". *
Square–cube law The square–cube law (or cube–square law) is a mathematical principle, applied in a variety of scientific fields, which describes the relationship between the volume and the surface area as a shape's size increases or decreases. It was first ...
(ratio of surface area to volume) *A 3/2-power law can be found in the plate characteristic curves of triodes. *The inverse-square laws of Newtonian gravity and
electrostatics Electrostatics is a branch of physics that studies electric charges at rest ( static electricity). Since classical times, it has been known that some materials, such as amber, attract lightweight particles after rubbing. The Greek word for amb ...
, as evidenced by the
gravitational potential In classical mechanics, the gravitational potential at a location is equal to the work (energy transferred) per unit mass that would be needed to move an object to that location from a fixed reference location. It is analogous to the electric p ...
and
Electrostatic potential Electrostatics is a branch of physics that studies electric charges at rest (static electricity). Since classical times, it has been known that some materials, such as amber, attract lightweight particles after rubbing. The Greek word for ambe ...
, respectively. * Self-organized criticality with a critical point as an
attractor In the mathematical field of dynamical systems, an attractor is a set of states toward which a system tends to evolve, for a wide variety of starting conditions of the system. System values that get close enough to the attractor values remain ...
*Model of van der Waals force *Force and potential in
simple harmonic motion In mechanics and physics, simple harmonic motion (sometimes abbreviated ) is a special type of periodic motion of a body resulting from a dynamic equilibrium between an inertial force, proportional to the acceleration of the body away from the ...
* Gamma correction relating light intensity with voltage * Behaviour near second-order phase transitions involving
critical exponent Critical or Critically may refer to: *Critical, or critical but stable, medical states **Critical, or intensive care medicine *Critical juncture, a discontinuous change studied in the social sciences. *Critical Software, a company specializing in ...
s *The
safe operating area For power semiconductor devices (such as BJT, MOSFET, thyristor or IGBT), the safe operating area (SOA) is defined as the voltage and current conditions over which the device can be expected to operate without self-damage. SOA is usually presen ...
relating to maximum simultaneous current and voltage in power semiconductors. *Supercritical state of matter and supercritical fluids, such as supercritical exponents of
heat capacity Heat capacity or thermal capacity is a physical property of matter, defined as the amount of heat to be supplied to an object to produce a unit change in its temperature. The SI unit of heat capacity is joule per kelvin (J/K). Heat capacity i ...
and viscosity. *The
Curie–von Schweidler law The Curie–von Schweidler law refers to the response of dielectric material to the step input of a direct current (DC) voltage first observed by Jacques Curie and Egon Ritter von Schweidler. Overview According to this law, the current decays acco ...
in dielectric responses to step DC voltage input. * The damping force over speed relation in antiseismic dampers calculus * Folded solvent-exposed surface areas of centered
amino acids Amino acids are organic compounds that contain both amino and carboxylic acid functional groups. Although hundreds of amino acids exist in nature, by far the most important are the alpha-amino acids, which comprise proteins. Only 22 alpha am ...
in
protein structure Protein structure is the three-dimensional arrangement of atoms in an amino acid-chain molecule. Proteins are polymers specifically polypeptides formed from sequences of amino acids, the monomers of the polymer. A single amino acid monomer ma ...
segments


Psychology

* Stevens's power law of psychophysics ( challenged with demonstrations that it may be logarithmic) * The power law of forgetting


Biology

*
Kleiber's law Kleiber's law, named after Max Kleiber for his biology work in the early 1930s, is the observation that, for the vast majority of animals, an animal's metabolic rate scales to the power of the animal's mass. Symbolically: if is the animal's met ...
relating animal metabolism to size, and
allometric law Allometry is the study of the relationship of body size to shape, anatomy, physiology and finally behaviour, first outlined by Otto Snell in 1892, by D'Arcy Thompson in 1917 in ''On Growth and Form'' and by Julian Huxley in 1932. Overview Allom ...
s in general * The two-thirds power law, relating speed to curvature in the human motor system. * The Taylor's law relating mean population size and variance of populations sizes in ecology *Neuronal avalanches * The
species richness Species richness is the number of different species represented in an ecological community, landscape or region. Species richness is simply a count of species, and it does not take into account the abundances of the species or their relative a ...
(number of species) in clades of freshwater fishes *The Harlow Knapp effect, where a subset of the kinases found in the human body compose a majority of published research *The size of forest patches globally follows a power law *The species-area relationship relating the number of species found in an area as a function of the size of the area


Meteorology

* The size of rain-shower cells, energy dissipation in cyclones, and the diameters of dust devils on Earth and Mars


General science

* Exponential growth and random observation (or killing) *Progress through exponential growth and exponential diffusion of innovations * Highly optimized tolerance *Proposed form of experience curve effects * Pink noise *The law of stream numbers, and the law of stream lengths ( Horton's laws describing river systems) *Populations of cities (
Gibrat's law Gibrat's law, sometimes called Gibrat's rule of proportionate growth or the law of proportionate effect, is a rule defined by Robert Gibrat (1904–1980) in 1931 stating that the proportional rate of growth of a firm is independent of its absolut ...
) * Bibliograms, and frequencies of words in a text ( Zipf's law) * 90–9–1 principle on wikis (also referred to as the 1% rule) *Richardson's Law for the severity of violent conflicts (wars and terrorism) *The relationship between a CPU's cache size and the number of cache misses follows the power law of cache misses. *The spectral density of the weight matrices of deep neural networks


Mathematics

*
Fractal In mathematics, a fractal is a geometric shape containing detailed structure at arbitrarily small scales, usually having a fractal dimension strictly exceeding the topological dimension. Many fractals appear similar at various scales, as il ...
s * Pareto distribution and the Pareto principle also called the "80–20 rule" * Zipf's law in corpus analysis and population distributions amongst others, where frequency of an item or event is inversely proportional to its frequency rank (i.e. the second most frequent item/event occurs half as often as the most frequent item, the third most frequent item/event occurs one third as often as the most frequent item, and so on). * Zeta distribution (discrete) *
Yule–Simon distribution In probability and statistics, the Yule–Simon distribution is a discrete probability distribution named after Udny Yule and Herbert A. Simon. Simon originally called it the ''Yule distribution''. The probability mass function (pmf) of the Yule ...
(discrete) * Student's t-distribution (continuous), of which the
Cauchy distribution The Cauchy distribution, named after Augustin Cauchy, is a continuous probability distribution. It is also known, especially among physicists, as the Lorentz distribution (after Hendrik Lorentz), Cauchy–Lorentz distribution, Lorentz(ian) func ...
is a special case * Lotka's law *The scale-free network model


Economics

* Population sizes of cities in a region or
urban network , also referred to as , is one of the Japan Railways Group (JR Group) companies and operates in western Honshu. It has its headquarters in Kita-ku, Osaka. It is listed in the Tokyo Stock Exchange, is a constituent of the TOPIX Large70 index, and ...
, Zipf's law. *Distribution of artists by the average price of their artworks. * Distribution of income in a market economy. *Distribution of degrees in banking networks.


Finance

* The mean absolute change of the logarithmic mid-prices * Number of tick counts over time * Size of the maximum price move * Average waiting time of a directional change * Average waiting time of an overshoot


Variants


Broken power law

A broken power law is a piecewise function, consisting of two or more power laws, combined with a threshold. For example, with two power laws: :f(x) \propto x^ for x :f(x) \propto x^_\textx^\text x>x_\text.


Power law with exponential cutoff

A power law with an exponential cutoff is simply a power law multiplied by an exponential function: :f(x) \propto x^e^.


Curved power law

:f(x) \propto x^


Power-law probability distributions

In a looser sense, a power-law
probability distribution In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon ...
is a distribution whose density function (or mass function in the discrete case) has the form, for large values of x, :P(X>x) \sim L(x) x^ where \alpha > 1, and L(x) is a slowly varying function, which is any function that satisfies \lim_ L(r\,x) / L(x) = 1 for any positive factor r. This property of L(x) follows directly from the requirement that p(x) be asymptotically scale invariant; thus, the form of L(x) only controls the shape and finite extent of the lower tail. For instance, if L(x) is the constant function, then we have a power law that holds for all values of x. In many cases, it is convenient to assume a lower bound x_ from which the law holds. Combining these two cases, and where x is a continuous variable, the power law has the form of the Pareto distribution :p(x) = \frac \left(\frac\right)^, where the pre-factor to \frac is the normalizing constant. We can now consider several properties of this distribution. For instance, its moments are given by :\langle x^ \rangle = \int_^\infty x^ p(x) \,\mathrmx = \fracx_\min^m which is only well defined for m < \alpha -1. That is, all moments m \geq \alpha - 1 diverge: when \alpha\leq 2, the average and all higher-order moments are infinite; when 2<\alpha<3, the mean exists, but the variance and higher-order moments are infinite, etc. For finite-size samples drawn from such distribution, this behavior implies that the
central moment In probability theory and statistics, a central moment is a moment of a probability distribution of a random variable about the random variable's mean; that is, it is the expected value of a specified integer power of the deviation of the random ...
estimators (like the mean and the variance) for diverging moments will never converge – as more data is accumulated, they continue to grow. These power-law probability distributions are also called Pareto-type distributions, distributions with Pareto tails, or distributions with regularly varying tails. A modification, which does not satisfy the general form above, with an exponential cutoff, is :p(x) \propto L(x) x^ \mathrm^. In this distribution, the exponential decay term \mathrm^ eventually overwhelms the power-law behavior at very large values of x. This distribution does not scale and is thus not asymptotically as a power law; however, it does approximately scale over a finite region before the cutoff. The pure form above is a subset of this family, with \lambda=0. This distribution is a common alternative to the asymptotic power-law distribution because it naturally captures finite-size effects. The
Tweedie distributions In probability and statistics, the Tweedie distributions are a family of probability distributions which include the purely continuous normal, gamma and inverse Gaussian distributions, the purely discrete scaled Poisson distribution, and the cl ...
are a family of statistical models characterized by closure under additive and reproductive convolution as well as under scale transformation. Consequently, these models all express a power-law relationship between the variance and the mean. These models have a fundamental role as foci of mathematical
convergence Convergence may refer to: Arts and media Literature *''Convergence'' (book series), edited by Ruth Nanda Anshen * "Convergence" (comics), two separate story lines published by DC Comics: **A four-part crossover storyline that united the four Wei ...
similar to the role that the
normal distribution In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is : f(x) = \frac e^ The parameter \mu i ...
has as a focus in the
central limit theorem In probability theory, the central limit theorem (CLT) establishes that, in many situations, when independent random variables are summed up, their properly normalized sum tends toward a normal distribution even if the original variables themselv ...
. This convergence effect explains why the variance-to-mean power law manifests so widely in natural processes, as with Taylor's law in ecology and with fluctuation scaling in physics. It can also be shown that this variance-to-mean power law, when demonstrated by the method of expanding bins, implies the presence of 1/''f'' noise and that 1/''f'' noise can arise as a consequence of this Tweedie convergence effect.


Graphical methods for identification

Although more sophisticated and robust methods have been proposed, the most frequently used graphical methods of identifying power-law probability distributions using random samples are Pareto quantile-quantile plots (or Pareto
Q–Q plot In statistics, a Q–Q plot (quantile-quantile plot) is a probability plot, a graphical method for comparing two probability distributions by plotting their '' quantiles'' against each other. A point on the plot corresponds to one of the qu ...
s), mean residual life plots and log–log plots. Another, more robust graphical method uses bundles of residual quantile functions. (Please keep in mind that power-law distributions are also called Pareto-type distributions.) It is assumed here that a random sample is obtained from a probability distribution, and that we want to know if the tail of the distribution follows a power law (in other words, we want to know if the distribution has a "Pareto tail"). Here, the random sample is called "the data". Pareto Q–Q plots compare the quantiles of the log-transformed data to the corresponding quantiles of an exponential distribution with mean 1 (or to the quantiles of a standard Pareto distribution) by plotting the former versus the latter. If the resultant scatterplot suggests that the plotted points " asymptotically converge" to a straight line, then a power-law distribution should be suspected. A limitation of Pareto Q–Q plots is that they behave poorly when the tail index \alpha (also called Pareto index) is close to 0, because Pareto Q–Q plots are not designed to identify distributions with slowly varying tails. On the other hand, in its version for identifying power-law probability distributions, the mean residual life plot consists of first log-transforming the data, and then plotting the average of those log-transformed data that are higher than the ''i''-th order statistic versus the ''i''-th order statistic, for ''i'' = 1, ..., ''n'', where n is the size of the random sample. If the resultant scatterplot suggests that the plotted points tend to "stabilize" about a horizontal straight line, then a power-law distribution should be suspected. Since the mean residual life plot is very sensitive to outliers (it is not robust), it usually produces plots that are difficult to interpret; for this reason, such plots are usually called Hill horror plots Log–log plots are an alternative way of graphically examining the tail of a distribution using a random sample. Caution has to be exercised however as a log–log plot is necessary but insufficient evidence for a power law relationship, as many non power-law distributions will appear as straight lines on a log–log plot. This method consists of plotting the logarithm of an estimator of the probability that a particular number of the distribution occurs versus the logarithm of that particular number. Usually, this estimator is the proportion of times that the number occurs in the data set. If the points in the plot tend to "converge" to a straight line for large numbers in the x axis, then the researcher concludes that the distribution has a power-law tail. Examples of the application of these types of plot have been published. A disadvantage of these plots is that, in order for them to provide reliable results, they require huge amounts of data. In addition, they are appropriate only for discrete (or grouped) data. Another graphical method for the identification of power-law probability distributions using random samples has been proposed. This methodology consists of plotting a ''bundle for the log-transformed sample''. Originally proposed as a tool to explore the existence of moments and the moment generation function using random samples, the bundle methodology is based on residual quantile functions (RQFs), also called residual percentile functions, which provide a full characterization of the tail behavior of many well-known probability distributions, including power-law distributions, distributions with other types of heavy tails, and even non-heavy-tailed distributions. Bundle plots do not have the disadvantages of Pareto Q–Q plots, mean residual life plots and log–log plots mentioned above (they are robust to outliers, allow visually identifying power laws with small values of \alpha, and do not demand the collection of much data). In addition, other types of tail behavior can be identified using bundle plots.


Plotting power-law distributions

In general, power-law distributions are plotted on doubly logarithmic axes, which emphasizes the upper tail region. The most convenient way to do this is via the (complementary)
cumulative distribution In statistics, the frequency (or absolute frequency) of an event i is the number n_i of times the observation has occurred/recorded in an experiment or study. These frequencies are often depicted graphically or in tabular form. Types The cumul ...
(ccdf) that is, the survival function, P(x) = \mathrm(X > x), :P(x) = \Pr(X > x) = C \int_x^\infty p(X)\,\mathrmX = \frac \int_x^\infty X^\,\mathrmX = \left(\frac \right)^. The cdf is also a power-law function, but with a smaller scaling exponent. For data, an equivalent form of the cdf is the rank-frequency approach, in which we first sort the n observed values in ascending order, and plot them against the vector \left ,\frac,\frac,\dots,\frac\right/math>. Although it can be convenient to log-bin the data, or otherwise smooth the probability density (mass) function directly, these methods introduce an implicit bias in the representation of the data, and thus should be avoided. The survival function, on the other hand, is more robust to (but not without) such biases in the data and preserves the linear signature on doubly logarithmic axes. Though a survival function representation is favored over that of the pdf while fitting a power law to the data with the linear least square method, it is not devoid of mathematical inaccuracy. Thus, while estimating exponents of a power law distribution, maximum likelihood estimator is recommended.


Estimating the exponent from empirical data

There are many ways of estimating the value of the scaling exponent for a power-law tail, however not all of them yield unbiased and consistent answers. Some of the most reliable techniques are often based on the method of
maximum likelihood In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statis ...
. Alternative methods are often based on making a linear regression on either the log–log probability, the log–log cumulative distribution function, or on log-binned data, but these approaches should be avoided as they can all lead to highly biased estimates of the scaling exponent.


Maximum likelihood

For real-valued,
independent and identically distributed In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usual ...
data, we fit a power-law distribution of the form : p(x) = \frac \left(\frac\right)^ to the data x\geq x_\min, where the coefficient \frac is included to ensure that the distribution is normalized. Given a choice for x_\min, the log likelihood function becomes: :\mathcal(\alpha)=\log \prod _^n \frac \left(\frac\right)^ The maximum of this likelihood is found by differentiating with respect to parameter \alpha, setting the result equal to zero. Upon rearrangement, this yields the estimator equation: :\hat = 1 + n \left \sum_^n \ln \frac \right where \ are the n data points x_\geq x_\min. This estimator exhibits a small finite sample-size bias of order O(n^), which is small when ''n'' > 100. Further, the standard error of the estimate is \sigma = \frac + O(n^). This estimator is equivalent to the popular Hill estimator from quantitative finance and extreme value theory. For a set of ''n'' integer-valued data points \, again where each x_i\geq x_\min, the maximum likelihood exponent is the solution to the transcendental equation : \frac = -\frac \sum_^n \ln \frac where \zeta(\alpha,x_) is the incomplete zeta function. The uncertainty in this estimate follows the same formula as for the continuous equation. However, the two equations for \hat are not equivalent, and the continuous version should not be applied to discrete data, nor vice versa. Further, both of these estimators require the choice of x_\min. For functions with a non-trivial L(x) function, choosing x_\min too small produces a significant bias in \hat\alpha, while choosing it too large increases the uncertainty in \hat, and reduces the
statistical power In statistics, the power of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H_0) when a specific alternative hypothesis (H_1) is true. It is commonly denoted by 1-\beta, and represents the chances ...
of our model. In general, the best choice of x_\min depends strongly on the particular form of the lower tail, represented by L(x) above. More about these methods, and the conditions under which they can be used, can be found in . Further, this comprehensive review article provide
usable code
(Matlab, Python, R and C++) for estimation and testing routines for power-law distributions.


Kolmogorov–Smirnov estimation

Another method for the estimation of the power-law exponent, which does not assume
independent and identically distributed In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usual ...
(iid) data, uses the minimization of the Kolmogorov–Smirnov statistic, D, between the cumulative distribution functions of the data and the power law: : \hat = \underset \, D_\alpha with : D_\alpha = \max_x , P_\mathrm(x) - P_\alpha(x) , where P_\mathrm(x) and P_\alpha(x) denote the cdfs of the data and the power law with exponent \alpha, respectively. As this method does not assume iid data, it provides an alternative way to determine the power-law exponent for data sets in which the temporal correlation can not be ignored.


Two-point fitting method

This criterion can be applied for the estimation of power-law exponent in the case of scale free distributions and provides a more convergent estimate than the maximum likelihood method. It has been applied to study probability distributions of fracture apertures. In some contexts the probability distribution is described, not by the
cumulative distribution function In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x. Ever ...
, by the cumulative frequency of a property ''X'', defined as the number of elements per meter (or area unit, second etc.) for which ''X'' > ''x'' applies, where ''x'' is a variable real number. As an example, the cumulative distribution of the fracture aperture, ''X'', for a sample of ''N'' elements is defined as 'the number of fractures per meter having aperture greater than ''x'' . Use of cumulative frequency has some advantages, e.g. it allows one to put on the same diagram data gathered from sample lines of different lengths at different scales (e.g. from outcrop and from microscope).


Validating power laws

Although power-law relations are attractive for many theoretical reasons, demonstrating that data does indeed follow a power-law relation requires more than simply fitting a particular model to the data. This is important for understanding the mechanism that gives rise to the distribution: superficially similar distributions may arise for significantly different reasons, and different models yield different predictions, such as extrapolation. For example,
log-normal distribution In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable is log-normally distributed, then has a normal ...
s are often mistaken for power-law distributions: a data set drawn from a lognormal distribution will be approximately linear for large values (corresponding to the upper tail of the lognormal being close to a power law), but for small values the lognormal will drop off significantly (bowing down), corresponding to the lower tail of the lognormal being small (there are very few small values, rather than many small values in a power law). For example,
Gibrat's law Gibrat's law, sometimes called Gibrat's rule of proportionate growth or the law of proportionate effect, is a rule defined by Robert Gibrat (1904–1980) in 1931 stating that the proportional rate of growth of a firm is independent of its absolut ...
about proportional growth processes produce distributions that are lognormal, although their log–log plots look linear over a limited range. An explanation of this is that although the logarithm of the lognormal density function is quadratic in , yielding a "bowed" shape in a log–log plot, if the quadratic term is small relative to the linear term then the result can appear almost linear, and the lognormal behavior is only visible when the quadratic term dominates, which may require significantly more data. Therefore, a log–log plot that is slightly "bowed" downwards can reflect a log-normal distribution – not a power law. In general, many alternative functional forms can appear to follow a power-law form for some extent. proposed plotting the empirical cumulative distribution function in the log-log domain and claimed that a candidate power-law should cover at least two orders of magnitude. Also, researchers usually have to face the problem of deciding whether or not a real-world probability distribution follows a power law. As a solution to this problem, Diaz proposed a graphical methodology based on random samples that allow visually discerning between different types of tail behavior. This methodology uses bundles of residual quantile functions, also called percentile residual life functions, which characterize many different types of distribution tails, including both heavy and non-heavy tails. However, claimed the need for both a statistical and a theoretical background in order to support a power-law in the underlying mechanism driving the data generating process. One method to validate a power-law relation tests many orthogonal predictions of a particular generative mechanism against data. Simply fitting a power-law relation to a particular kind of data is not considered a rational approach. As such, the validation of power-law claims remains a very active field of research in many areas of modern science.


See also

* Fat-tailed distribution * Heavy-tailed distributions *
Hyperbolic growth When a quantity grows towards a singularity under a finite variation (a " finite-time singularity") it is said to undergo hyperbolic growth. More precisely, the reciprocal function 1/x has a hyperbola as a graph, and has a singularity at 0, me ...
*
Lévy flight A Lévy flight is a random walk in which the step-lengths have a Lévy distribution, a probability distribution that is heavy-tailed. When defined as a walk in a space of dimension greater than one, the steps made are in isotropic random directi ...
* Long tail * Pareto distribution * Power law fluid * Simon model * Stable distribution * Stevens's power law


References

Notes Bibliography * * * * * * * * * *


External links


Zipf, Power-laws, and Pareto – a ranking tutorial


by Benoit Mandelbrot & Nassim Nicholas Taleb. ''Fortune'', July 11, 2005.
"Million-dollar Murray"
power-law distributions in homelessness and other social problems; by
Malcolm Gladwell Malcolm Timothy Gladwell (born 3 September 1963) is an English-born Canadian journalist, author, and public speaker. He has been a staff writer for ''The New Yorker'' since 1996. He has published seven books: '' The Tipping Point: How Little T ...
. ''The New Yorker'', February 13, 2006. *Benoit Mandelbrot & Richard Hudson: ''The Misbehaviour of Markets (2004)'' *Philip Ball
Critical Mass: How one thing leads to another
(2005)

fro
The Econophysics Blog''So You Think You Have a Power Law – Well Isn't That Special?''
fro
Three-Toed Sloth
the blog of Cosma Shalizi, Professor of Statistics at Carnegie-Mellon University.
Simple MATLAB script
which bins data to illustrate power-law distributions (if any) in the data.
The Erdős Webgraph Server
visualizes the distribution of the degrees of the webgraph on th
download page
{{DEFAULTSORT:Power Law Exponentials Theory of probability distributions Statistical laws Articles with example R code