Pareto distribution

The Pareto distribution, named after the Italian civil engineer, economist, and sociologist Vilfredo Pareto, is a power-law probability distribution that is used in description of social, quality control, scientific, geophysical, actuarial, and many other types of observable phenomena; the principle originally applied to describing the distribution of wealth in a society, fitting the trend that a large portion of wealth is held by a small fraction of the population. The Pareto principle or "80-20 rule" stating that 80% of outcomes are due to 20% of causes was named in honour of Pareto, but the concepts are distinct, and only Pareto distributions with shape value (α) of log₄5 ≈ 1.16 precisely reflect it. Empirical observation has shown that this 80-20 distribution fits a wide range of cases, including natural phenomena and human activities.

Pareto Type I

Probability density function Pareto Type I probability density functions for various ${\displaystyle \alpha }$ with ${\displaystyle x_{\mathrm {m} }=1.}$ As ${\displaystyle \alpha \rightarrow \infty ,}$ the distribution approaches ${\displaystyle \delta (x-x_{\mathrm {m} }),}$ where ${\displaystyle \delta }$ is the Dirac delta function.
Cumulative distribution function Pareto Type I cumulative distribution functions for various ${\displaystyle \alpha }$ with ${\displaystyle x_{\mathrm {m} }=1.}$
Parameters	${\displaystyle x_{\mathrm {m} }>0}$ scale (real) ${\displaystyle \alpha >0}$ shape (real)
Support	${\displaystyle x\in [x_{\mathrm {m} },\infty )}$
PDF	${\displaystyle {\frac {\alpha x_{\mathrm {m} }^{\alpha }}{x^{\alpha +1}}}}$
CDF	${\displaystyle 1-\left({\frac {x_{\mathrm {m} }}{x}}\right)^{\alpha }}$
Quantile	${\displaystyle x_{\mathrm {m} }{(1-p)}^{-{\frac {1}{\alpha }}}}$
Mean	${\displaystyle {\begin{cases}\infty &{\text{for }}\alpha \leq 1\\{\dfrac {\alpha x_{\mathrm {m} }}{\alpha -1}}&{\text{for }}\alpha >1\end{cases}}}$
Median	${\displaystyle x_{\mathrm {m} }{\sqrt[{\alpha }]{2}}}$
Mode	${\displaystyle x_{\mathrm {m} }}$
Variance	${\displaystyle {\begin{cases}\infty &{\text{for }}\alpha \leq 2\\{\dfrac {x_{\mathrm {m} }^{2}\alpha }{(\alpha -1)^{2}(\alpha -2)}}&{\text{for }}\alpha >2\end{cases}}}$
Skewness	${\displaystyle {\frac {2(1+\alpha )}{\alpha -3}}{\sqrt {\frac {\alpha -2}{\alpha }}}{\text{ for }}\alpha >3}$
Ex. kurtosis	${\displaystyle {\frac {6(\alpha ^{3}+\alpha ^{2}-6\alpha -2)}{\alpha (\alpha -3)(\alpha -4)}}{\text{ for }}\alpha >4}$
Entropy	${\displaystyle \log \left(\left({\frac {x_{\mathrm {m} }}{\alpha }}\right)\,e^{1+{\tfrac {1}{\alpha }}}\right)}$
MGF	does not exist
CF	${\displaystyle \alpha (-ix_{\mathrm {m} }t)^{\alpha }\Gamma (-\alpha ,-ix_{\mathrm {m} }t)}$
Fisher information	${\displaystyle {\mathcal {I}}(x_{\mathrm {m} },\alpha )={\begin{bmatrix}{\dfrac {\alpha ^{2}}{x_{\mathrm {m} }^{2}}}&0\\0&{\dfrac {1}{\alpha ^{2}}}\end{bmatrix}}}$
Expected shortfall	${\displaystyle {\frac {x_{m}\alpha }{(1-p)^{\frac {1}{\alpha }}(\alpha -1)}}}$