Landau distribution

Short description: Probability distribution

Landau distribution

Probability density function [[File:350px $\mu =0,c=\pi /2$|frameless]]
Parameters	$c\in (0,\mathrm{\infty })$ — scale parameter $\mu \in (-\mathrm{\infty },\mathrm{\infty })$ — location parameter
Support	$\mathbb{ℝ}$
PDF	$\frac{1}{\pi c}\underset{0}{\overset{\mathrm{\infty }}{\int }}{e}^{-t}\cos ((x-\mu )\frac{t}{c}+\frac{2t}{\pi }\log \left(\frac{t}{c}\right))dt$
Mean	Undefined
Variance	Undefined
MGF	Undefined
CF	$\exp (it\mu -\frac{2ict}{\pi }\log |t|-c|t|)$

In probability theory, the Landau distribution is a probability distribution named after Lev Landau. Because of the distribution's "fat" tail, the moments of the distribution, such as mean or variance, are undefined. The distribution is a particular case of stable distribution.

Definition

The probability density function, as written originally by Landau, is defined by the complex integral:

$$p(x)=\frac{1}{2\pi i}\underset{a-i\mathrm{\infty }}{\overset{a+i\mathrm{\infty }}{\int }}{e}^{s\log (s)+xs}ds,$$

where a is an arbitrary positive real number, meaning that the integration path can be any parallel to the imaginary axis, intersecting the real positive semi-axis, and $\log$ refers to the natural logarithm. In other words it is the Laplace transform of the function $s^s$.

The following real integral is equivalent to the above:

$$p(x)=\frac{1}{\pi }\underset{0}{\overset{\mathrm{\infty }}{\int }}{e}^{-t\log (t)-xt}\sin (\pi t)dt.$$

The full family of Landau distributions is obtained by extending the original distribution to a location-scale family of stable distributions with parameters $\alpha =1$ and $\beta =1$, with characteristic function:

$$\phi (t;\mu ,c)=\exp (it\mu -\frac{2ict}{\pi }\log |t|-c|t|)$$

where $c\in (0,\mathrm{\infty })$ and $\mu \in (-\mathrm{\infty },\mathrm{\infty })$, which yields a density function:

$$p(x;\mu ,c)=\frac{1}{\pi c}\underset{0}{\overset{\mathrm{\infty }}{\int }}{e}^{-t}\cos ((x-\mu )\frac{t}{c}+\frac{2t}{\pi }\log \left(\frac{t}{c}\right))dt,$$

Taking $\mu =0$ and $c=\frac{\pi }{2}$ we get the original form of $p(x)$ above.

Properties

The approximation function for $\mu =0,c=1$

• Translation: If $X\sim \text{<mrow data-mjx-texclass="ORD">Landau</mrow>}(\mu ,c)$ then $X+m\sim \text{<mrow data-mjx-texclass="ORD">Landau</mrow>}(\mu +m,c)$.
• Scaling: If $X\sim \text{<mrow data-mjx-texclass="ORD">Landau</mrow>}(\mu ,c)$ then $aX\sim \text{<mrow data-mjx-texclass="ORD">Landau</mrow>}(a\mu -\frac{2}{\pi }ac\log (a),ac)$.
• Sum: If $X\sim \text{<mrow data-mjx-texclass="ORD">Landau</mrow>}({\mu }_1,c_1)$ and $Y\sim \text{<mrow data-mjx-texclass="ORD">Landau</mrow>}({\mu }_2,c_2)$ then $X+Y\sim \text{<mrow data-mjx-texclass="ORD">Landau</mrow>}({\mu }_1+{\mu }_2,c_1+c_2)$.

These properties can all be derived from the characteristic function. Together they imply that the Landau distributions are closed under affine transformations.

Approximations

In the "standard" case $\mu =0$ and $c=\pi /2$, the pdf can be approximated using Lindhard theory which says:

$$p(x+\log (x)-1+\gamma )\approx \frac{\exp (-1/x)}{x(1+x)},$$

where $\gamma$ is Euler's constant.

A similar approximation of $p(x;\mu ,c)$ for $\mu =0$ and $c=1$ is:

$$p(x)\approx \frac{1}{\sqrt{2\pi }}\exp (-\frac{x+e^{-x}}{2}).$$

Related distributions

• The Landau distribution is a stable distribution with stability parameter $\alpha$ and skewness parameter $\beta$ both equal to 1.