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 Probability density function  Pareto probability density functions for various α (labeled "k") with xm = 1. The horizontal axis is the x  parameter. As α → ∞ the distribution approaches δ(x − xm) where δ is the Dirac delta function. Cumulative distribution function  Pareto cumulative distribution functions for various α(labeled "k") with xm = 1. The horizontal axis is the x  parameter. Parameters $x_\mathrm{m}>0\,$ scale (real)$\alpha>0\,$ shape (real) Support $x \in [x_\mathrm{m}, +\infty)\!$ pdf $\frac{\alpha\,x_\mathrm{m}^\alpha}{x^{\alpha+1}}\text{ for }x\ge x_m\!$ cdf $1-\left(\frac{x_\mathrm{m}}{x}\right)^\alpha \text{ for } x \ge x_m\!$ Mean $\frac{\alpha\,x_\mathrm{m}}{\alpha-1}\text{ for }\alpha>1\,$ Median $x_\mathrm{m} \sqrt[\alpha]{2}$ Mode $x_\mathrm{m}\,$ Variance $\frac{x_\mathrm{m}^2\alpha}{(\alpha-1)^2(\alpha-2)}\text{ for }\alpha>2\,$ Skewness $\frac{2(1+\alpha)}{\alpha-3}\,\sqrt{\frac{\alpha-2}{\alpha}}\text{ for }\alpha>3\,$ Kurtosis $\frac{6(\alpha^3+\alpha^2-6\alpha-2)}{\alpha(\alpha-3)(\alpha-4)}\text{ for }\alpha>4\,$ Entropy $\ln\left(\frac{x_\mathrm{m}}{\alpha}\right) + \frac{1}{\alpha} + 1\!$ mgf $\alpha(-x_\mathrm{m}t)^\alpha\Gamma(-\alpha,-x_\mathrm{m}t)\text{ for }t<0\,$ Char. func. $\alpha(-ix_\mathrm{m}t)^\alpha\Gamma(-\alpha,-ix_\mathrm{m}t)\,$

The Pareto distribution, named after the Italian economist Vilfredo Pareto, is a power law probability distribution that coincides with social, scientific, and many other types of observable phenomena. Outside the field of economics it is sometimes referred to as the Bradford distribution.

## Definition Edit

If X is a random variable with a Pareto (Type I) distribution, then the probability that X is greater than some number x is given by

$\Pr(X>x) = \begin{cases} \left(\frac{x_\mathrm{m}}{x}\right)^\alpha & \text{for }x\ge x_\mathrm{m}, \\ 1 & \text{for } x < x_\mathrm{m}. \end{cases}$

where xm is the (necessarily positive) minimum possible value of X, and α is a positive parameter. The family of Pareto distributions is parameterized by two quantities, xm and α. When this distribution is used to model the distribution of wealth, then the parameter α is called the Pareto index.

## Properties Edit

### Cumulative distribution function Edit

From the definition, the cumulative distribution function of a Pareto random variable with parameters α and xm is

$F_X(x) = \begin{cases} 1-\left(\frac{x_\mathrm{m}}{x}\right)^\alpha & \text{for } x \ge x_\mathrm{m}, \\ 0 & \text{for }x < x_\mathrm{m}. \end{cases}$

### Graphical representation Edit

When plotted on linear axes, the distribution assumes the familiar J-shaped curve which approaches each of the orthogonal axes asymptotically. All segments of the curve are self-similar (subject to appropriate scaling factors).

When plotted on logarithmic scales (both axes logarithmic), the distribution is represented by a straight line.

### Probability density function Edit

It follows (by differentiation) that the probability density function is

$f_X(x)= \begin{cases} \alpha\,\dfrac{x_\mathrm{m}^\alpha}{x^{\alpha+1}} & \text{for }x > x_\mathrm{m}, \\[12pt] 0 & \text{for } x < x_\mathrm{m}. \end{cases}$

### Moments and characteristic function Edit

$E(X)=\frac{\alpha x_\mathrm{m}}{\alpha-1} \,$
(if α ≤ 1, the expected value does not exist).
$\mathrm{Var}(X)=\left(\frac{x_\mathrm{m}}{\alpha-1}\right)^2 \frac{\alpha}{\alpha-2}.$
(If α ≤ 2, the variance does not exist.)
$\mu_n'=\frac{\alpha x_\mathrm{m}^n}{\alpha-n}, \,$
but the nth moment exists only for n < α.
$M\left(t,\alpha,x_\mathrm{m}\right) = E(e^{tX}) = \alpha(-x_\mathrm{m} t)^\alpha\Gamma(-\alpha,-x_\mathrm{m} t)\text{ and }M\left(0,\alpha,x_\mathrm{m}\right)=1.\,$
$\varphi(t;\alpha,x_\mathrm{m})=\alpha(-ix_\mathrm{m} t)^\alpha\Gamma(-\alpha,-ix_\mathrm{m} t),$
where Γ(ax) is the incomplete gamma function.

### Degenerate case Edit

The Dirac delta function is a limiting case of the Pareto density:

$\lim_{\alpha\rightarrow \infty} f(x;\alpha,x_\mathrm{m})=\delta(x-x_\mathrm{m}). \,$

### Conditional distributions Edit

The conditional probability distribution of a Pareto-distributed random variable, given the event that it is greater than or equal to a particular number x1 exceeding xm, is a Pareto distribution with the same Pareto index α but with minimum x1 instead of xm.

### A characterization theorem Edit

Suppose Xi, i = 1, 2, 3, ... are independent identically distributed random variables whose probability distribution is supported on the interval [xm, ∞) for some xm > 0. Suppose that for all n, the two random variables min{ X1, ..., Xn } and (X1 + ... + Xn)/min{ X1, ..., Xn } are independent. Then the common distribution is a Pareto distribution.

## Applications Edit

Pareto originally used this distribution to describe the allocation of wealth among individuals since it seemed to show rather well the way that a larger portion of the wealth of any society is owned by a smaller percentage of the people in that society. He also used it to describe distribution of income. This idea is sometimes expressed more simply as the Pareto principle or the "80-20 rule" which says that 20% of the population controls 80% of the wealth. However, the 80-20 rule corresponds to a particular value of α, and in fact, Pareto's data on British income taxes in his Cours d'économie politique indicates that about 30% of the population had about 70% of the income. The probability density function (PDF) graph at the beginning of this article shows that the "probability" or fraction of the population that owns a small amount of wealth per person is rather high, and then decreases steadily as wealth increases. This distribution is not limited to describing wealth or income, but to many situations in which an equilibrium is found in the distribution of the "small" to the "large". The following examples are sometimes seen as approximately Pareto-distributed:

• The sizes of human settlements (few cities, many hamlets/villages)
• The standardized price returns on individual stocks 
• Numbers of species per genus (There is subjectivity involved: The tendency to divide a genus into two or more increases with the number of species in it)[citation needed]
• Severity of large casualty losses for certain lines of business such as general liability, commercial auto, and workers compensation.

## Relation to other distributions Edit

### Relation to the exponential distribution Edit

The Pareto distribution is related to the exponential distribution as follows. If X is Pareto-distributed with minimum xm and index α, then

$Y = \log\left(\frac{X}{x_\mathrm{m}}\right).$

is exponentially distributed with intensity (rate parameter) α. Equivalently, if Y is exponentially distributed with intensity α, then

$x_\mathrm{m} e^Y \,$

is Pareto-distributed with minimum xm and index α.

This can be shown using the standard change of variable techniques:

$\Pr(Y<y) = \Pr\left(\log\left(\frac{X}{x_\mathrm{m}}\right)<y\right) = \Pr(X<x_\mathrm{m} e^y) = 1-\left(\frac{x_\mathrm{m}}{x_\mathrm{m}e^y}\right)^\alpha=1-e^{-\alpha y}.$

The last expression is the cumulative distribution function of an exponential distribution with intensity α.

### Relation to the log-normal distribution Edit

Note that the Pareto distribution and log-normal distribution are alternative distributions for describing the same types of quantities. One of the connections between the two is that they are both the distributions of the exponential of random variables distributed according to other common distributions, respectively the exponential distribution and normal distribution. (Both of these latter two distributions are "basic" in the sense that the logarithms of their density functions are linear and quadratic, respectively, functions of the observed values.)[citation needed]

### Relation to the generalized Pareto distribution Edit

The Pareto distribution is a special case of the generalized Pareto distribution, which is a family of distributions of similar form, but containing an extra parameter in such a way that the support of the distribution is either bounded below (at a variable point), or bounded both above and below (where both are variable), with the Lomax distribution as a special case. This family also contains both the unshifted and shifted exponential distributions.

### Relation to Zipf's law Edit

Pareto distributions are continuous probability distributions. Zipf's law, also sometimes called the zeta distribution, may be thought of as a discrete counterpart of the Pareto distribution.

### Relation to the "Pareto principle" Edit

The "80-20 law", according to which 20% of all people receive 80% of all income, and 20% of the most affluent 20% receive 80% of that 80%, and so on, holds precisely when the Pareto index is α = log45, approximately 1.161. Moreover, the following have been shown to be mathematically equivalent:

• Income is distributed according to a Pareto distribution with index α > 1.
• There is some number 0 ≤ p ≤ 1/2 such that 100p% of all people receive 100(1 − p)% of all income, and similarly for every real (not necessarily integer) n > 0, 100pn% of all people receive 100(1 − p)n% of all income.

This does not apply only to income, but also to wealth, or to anything else that can be modeled by this distribution.

This excludes Pareto distributions in which 0 < α ≤ 1, which, as noted above, have infinite expected value, and so cannot reasonably model income distribution.

## Pareto, Lorenz, and Gini Edit Lorenz curves for a number of Pareto distributions. The case α = ∞ corresponds to perfectly equal distribution (G = 0) and the α = 1 line corresponds to complete inequality (G = 1)

The Lorenz curve is often used to characterize income and wealth distributions. For any distribution, the Lorenz curve L(F) is written in terms of the PDF ƒ or the CDF F as

$L(F)=\frac{\int_{x_\mathrm{m}}^{x(F)} xf(x)\,dx}{\int_{x_\mathrm{m}}^\infty xf(x)\,dx} =\frac{\int_0^F x(F')\,dF'}{\int_0^1 x(F')\,dF'}$

where x(F) is the inverse of the CDF. For the Pareto distribution,

$x(F)=\frac{x_\mathrm{m}}{(1-F)^{1/\alpha}}$

and the Lorenz curve is calculated to be

$L(F) = 1-(1-F)^{1-1/\alpha},\,$

where α must be greater than or equal to unity, since the denominator in the expression for L(F) is just the mean value of x. Examples of the Lorenz curve for a number of Pareto distributions are shown in the graph on the right.

The Gini coefficient is a measure of the deviation of the Lorenz curve from the equidistribution line which is a line connecting [0, 0] and [1, 1], which is shown in black (α = ∞) in the Lorenz plot on the right. Specifically, the Gini coefficient is twice the area between the Lorenz curve and the equidistribution line. The Gini coefficient for the Pareto distribution is then calculated to be

$G = 1-2\int_0^1L(F)\,dF = \frac{1}{2\alpha-1}$ (?? - Talk:Pareto distribution#Gini coeff)

(see Aaberge 2005).

## Parameter estimation Edit

The likelihood function for the Pareto distribution parameters α and xm, given a sample x = (x1x2, ..., xn), is

$L(\alpha, x_\mathrm{m}) = \prod _{i=1}^n \alpha \frac {x_\mathrm{m}^\alpha} {x_i^{\alpha+1}} = \alpha^n x_\mathrm{m}^{n\alpha} \prod _{i=1}^n \frac 1 {x_i^{\alpha+1}}. \!$

Therefore, the logarithmic likelihood function is

$\ell(\alpha, x_\mathrm{m}) = n \ln \alpha + n\alpha \ln x_\mathrm{m} - (\alpha + 1) \sum _{i=1} ^n \ln x_i. \!$

It can be seen that $\ell(\alpha, x_\mathrm{m})$ is monotonically increasing with $x_\mathrm{m}$, that is, the greater the value of $x_\mathrm{m}$, the greater the value of the likelihood function. Hence, since $x \ge x_\mathrm{m}$, we conclude that

$\widehat x_\mathrm{m} = \min_i {x_i}.$

To find the estimator for α, we compute the corresponding partial derivative and determine where it is zero:

$\frac{\partial \ell}{\partial \alpha} = \frac{n}{\alpha} + n \ln x_\mathrm{m} - \sum _{i=1}^n \ln x_i = 0.$

Thus the maximum likelihood estimator for α is:

$\widehat \alpha = \frac{n}{\sum _i \left( \ln x_i - \ln \widehat x_\mathrm{m} \right)}.$

The expected statistical error is:

$\sigma = \frac {\widehat \alpha} {\sqrt n}.$

## Graphical representation Edit

The characteristic curved 'long tail' distribution when plotted on a linear scale, masks the underlying simplicity of the function when plotted on a log-log graph, which then takes the form of a straight line with negative gradient.[citation needed]

## Generating a random sample from Pareto distribution Edit

Random samples can be generated using inverse transform sampling. Given a random variate U drawn from the uniform distribution on the unit interval (0, 1], the variate T given by

$T=\frac{x_\mathrm{m}}{U^{1/\alpha}}$

is Pareto-distributed.[citation needed] If U is uniformly distributed on [0, 1), it can be exchanged for (1 - U).

## Bounded Pareto distribution Edit

 Probability density function Cumulative distribution function Parameters $L > 0 \,$ location (real) $H > L \,$ location (real) $\alpha > 0 \,$ shape (real) Support $L \leqslant x \leqslant H \,$ pdf $\frac{\alpha L^\alpha x^{-\alpha - 1}}{1-\left(\frac{L}{H}\right)^\alpha}$ cdf $\frac{1-L^\alpha x^{-\alpha}}{1-\left(\frac{L}{H}\right)^\alpha}$ Mean $\frac{L^\alpha}{1 - \left(\frac{L}{H}\right)^\alpha} \cdot \left(\frac{\alpha}{\alpha-1}\right) \cdot \left(\frac{1}{L^{\alpha-1}} - \frac{1}{H^{\alpha-1}}\right), \alpha\neq 1$ Median $L \left(1- \frac{1}{2}\left(1-\left(\frac{L}{H}\right)^\alpha\right)\right)^{-\frac{1}{\alpha}}$ Mode Variance $\frac{L^\alpha}{1 - \left(\frac{L}{H}\right)^\alpha} \cdot \left(\frac{\alpha}{\alpha-2}\right) \cdot \left(\frac{1}{L^{\alpha-2}} - \frac{1}{H^{\alpha-2}}\right), \alpha\neq 2$ Skewness Kurtosis Entropy mgf Char. func.

The bounded Pareto distribution or truncated Pareto distribution has three parameters α, L and H. As in the standard Pareto distribution α determines the shape. L denotes the minimal value, and H denotes the maximal value. (The Variance in the table on the right should be interpreted as 2nd Moment).

$\frac{\alpha L^\alpha x^{-\alpha - 1}}{1-\left(\frac{L}{H}\right)^\alpha}$

where L ≤ x ≤ H, and α > 0.

### Generating bounded Pareto random variables Edit

If U is uniformly distributed on (0, 1), then

$\left(-\frac{U H^\alpha - U L^\alpha - H^\alpha}{H^\alpha L^\alpha}\right)^{-\frac{1}{\alpha}}$

is bounded Pareto-distributed.[citation needed]

## Symmetric Pareto distribution Edit

The symmetric Pareto distribution can be defined by the probability density function:

$f(x;\alpha,x_\mathrm{m}) = \begin{cases} (\alpha x_\mathrm{m}^\alpha/2) |x|^{-\alpha-1} & \text{for }|x|>x_\mathrm{m} \\ 0 & \text{otherwise}. \end{cases}$

It has a similar shape to a Pareto distribution for $x > x_\mathrm{m}$ while looking like an inverted Pareto distribution for $x < x_\mathrm{m}$[citation needed].

## Edit

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