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) function, or Breit–Wigner distribution. The Cauchy distribution $f(x;x_{0},\gamma )$ is the distribution of the x-intercept of a ray issuing from $(x_{0},\gamma )$ with a uniformly distributed angle. It is also the distribution of the ratio of two independent normally distributed random variables with mean zero.
The Cauchy distribution is often used in statistics as the canonical example of a "pathological" distribution since both its expected value and its variance are undefined (but see § Explanation of undefined moments below). The Cauchy distribution does not have finite moments of order greater than or equal to one; only fractional absolute moments exist.^{[1]} The Cauchy distribution has no moment generating function.
It is one of the few distributions that is stable and has a probability density function that can be expressed analytically, the others being the normal distribution and the Lévy distribution.
History
Estimating the mean and standard deviation through samples from a Cauchy distribution (bottom) does not converge with more samples, as in the normal distribution (top). There can be arbitrarily large jumps in the estimates, as seen in the graphs on the bottom. (Click to expand)
A function with the form of the density function of the Cauchy distribution was studied geometrically by Fermat in 1659, and later was known as the witch of Agnesi, after Agnesi included it as an example in her 1748 calculus textbook. Despite its name, the first explicit analysis of the properties of the Cauchy distribution was published by the French mathematician Poisson in 1824, with Cauchy only becoming associated with it during an academic controversy in 1853.^{[2]} Poisson noted that if the mean of observations following such a distribution were taken, the mean error^{[further explanation needed]} did not converge to any finite number. As such, Laplace's use of the central limit theorem with such distribution was inappropriate, as it assumed a finite mean and variance. Despite this, Poisson did not regard the issue as important, in contrast to Bienaymé, who was to engage Cauchy in a long dispute over the matter.
where $I$ is the height of the peak. The three-parameter Lorentzian function indicated is not, in general, a probability density function, since it does not integrate to 1, except in the special case where $I={\frac {1}{\pi \gamma )).\!$
The Cauchy distribution is an example of a distribution which has no mean, variance or higher moments defined. Its mode and median are well defined and are both equal to $x_{0))$.
If $\Sigma$ is a $p\times p$ positive-semidefinite covariance matrix with strictly positive diagonal entries, then for independent and identically distributed$X,Y\sim N(0,\Sigma )$ and any random $p$-vector $w$ independent of $X$ and $Y$ such that $w_{1}+\cdots +w_{p}=1$ and $w_{i}\geq 0,i=1,\ldots ,p,$ (defining a categorical distribution) it holds that
If $X_{1},\ldots ,X_{n))$ are independent and identically distributed random variables, each with a standard Cauchy distribution, then the sample mean$(X_{1}+\cdots +X_{n})/n$ has the same standard Cauchy distribution. To see that this is true, compute the characteristic function of the sample mean:
where ${\overline {X))$ is the sample mean. This example serves to show that the condition of finite variance in the central limit theorem cannot be dropped. It is also an example of a more generalized version of the central limit theorem that is characteristic of all stable distributions, of which the Cauchy distribution is a special case.
which is just the Fourier transform of the probability density. The original probability density may be expressed in terms of the characteristic function, essentially by using the inverse Fourier transform:
The nth moment of a distribution is the nth derivative of the characteristic function evaluated at $t=0$. Observe that the characteristic function is not differentiable at the origin: this corresponds to the fact that the Cauchy distribution does not have well-defined moments higher than the zeroth moment.
Comparison with the normal distribution
Compared to the normal distribution, the Cauchy density function has a higher peak and lower tails.
An example is shown in the two figures added here
Observed histogram and best fitting Cauchy density function.^{[13]}
Observed histogram and best fitting normal density function.^{[13]}
The figure to the left shows the Cauchy probability density function fitted to an observed histogram. The peak of the function is higher than the peak of the histogram while the tails are lower than those of the histogram.
The figure to the right shows the normal probability density function fitted to the same observed histogram. The peak of the function is lower than the peak of the histogram.
This illustrates the above statement.
For the integral to exist (even as an infinite value), at least one of the terms in this sum should be finite, or both should be infinite and have the same sign. But in the case of the Cauchy distribution, both the terms in this sum (2) are infinite and have opposite sign. Hence (1) is undefined, and thus so is the mean.^{[14]}
The Cauchy distribution does not have finite moments of any order. Some of the higher raw moments do exist and have a value of infinity, for example, the raw second moment:
By re-arranging the formula, one can see that the second moment is essentially the infinite integral of a constant (here 1). Higher even-powered raw moments will also evaluate to infinity. Odd-powered raw moments, however, are undefined, which is distinctly different from existing with the value of infinity. The odd-powered raw moments are undefined because their values are essentially equivalent to $\infty -\infty$ since the two halves of the integral both diverge and have opposite signs. The first raw moment is the mean, which, being odd, does not exist. (See also the discussion above about this.) This in turn means that all of the central moments and standardized moments are undefined since they are all based on the mean. The variance—which is the second central moment—is likewise non-existent (despite the fact that the raw second moment exists with the value infinity).
The results for higher moments follow from Hölder's inequality, which implies that higher moments (or halves of moments) diverge if lower ones do.
Moments of truncated distributions
Consider the truncated distribution defined by restricting the standard Cauchy distribution to the interval [−10^{100}, 10^{100}]. Such a truncated distribution has all moments (and the central limit theorem applies for i.i.d. observations from it); yet for almost all practical purposes it behaves like a Cauchy distribution.^{[15]}
Estimation of parameters
Because the parameters of the Cauchy distribution do not correspond to a mean and variance, attempting to estimate the parameters of the Cauchy distribution by using a sample mean and a sample variance will not succeed.^{[16]} For example, if an i.i.d. sample of size n is taken from a Cauchy distribution, one may calculate the sample mean as:
${\bar {x))={\frac {1}{n))\sum _{i=1}^{n}x_{i))$
Although the sample values $x_{i))$ will be concentrated about the central value $x_{0))$, the sample mean will become increasingly variable as more observations are taken, because of the increased probability of encountering sample points with a large absolute value. In fact, the distribution of the sample mean will be equal to the distribution of the observations themselves; i.e., the sample mean of a large sample is no better (or worse) an estimator of $x_{0))$ than any single observation from the sample. Similarly, calculating the sample variance will result in values that grow larger as more observations are taken.
Therefore, more robust means of estimating the central value $x_{0))$ and the scaling parameter $\gamma$ are needed. One simple method is to take the median value of the sample as an estimator of $x_{0))$ and half the sample interquartile range as an estimator of $\gamma$. Other, more precise and robust methods have been developed ^{[17]}^{[18]} For example, the truncated mean of the middle 24% of the sample order statistics produces an estimate for $x_{0))$ that is more efficient than using either the sample median or the full sample mean.^{[19]}^{[20]} However, because of the fat tails of the Cauchy distribution, the efficiency of the estimator decreases if more than 24% of the sample is used.^{[19]}^{[20]}
Maximum likelihood can also be used to estimate the parameters $x_{0))$ and $\gamma$. However, this tends to be complicated by the fact that this requires finding the roots of a high degree polynomial, and there can be multiple roots that represent local maxima.^{[21]} Also, while the maximum likelihood estimator is asymptotically efficient, it is relatively inefficient for small samples.^{[22]}^{[23]} The log-likelihood function for the Cauchy distribution for sample size $n$ is:
Maximizing the log likelihood function with respect to $x_{0))$ and $\gamma$ by taking the first derivative produces the following system of equations:
Solving just for $x_{0))$ requires solving a polynomial of degree $2n-1$,^{[21]} and solving just for $\,\!\gamma$ requires solving a polynomial of degree $2n$. Therefore, whether solving for one parameter or for both parameters simultaneously, a numerical solution on a computer is typically required. The benefit of maximum likelihood estimation is asymptotic efficiency; estimating $x_{0))$ using the sample median is only about 81% as asymptotically efficient as estimating $x_{0))$ by maximum likelihood.^{[20]}^{[24]} The truncated sample mean using the middle 24% order statistics is about 88% as asymptotically efficient an estimator of $x_{0))$ as the maximum likelihood estimate.^{[20]} When Newton's method is used to find the solution for the maximum likelihood estimate, the middle 24% order statistics can be used as an initial solution for $x_{0))$.
The shape can be estimated using the median of absolute values, since for location 0 Cauchy variables $X\sim \mathrm {Cauchy} (0,\gamma )$, the $\mathrm {median} (|X|)=\gamma$ the shape parameter.
Generating values from Cauchy distribution
Let $u$ be a sample from a uniform distribution from $[0,1]$, then we can generate a sample, $x$ from Cauchy distribution using
$x=\tan \left(\pi (u-{\frac {1}{2)))\right)$
Alternatively, the ratio of two standard normally distributed samples is a Cauchy sample.
Multivariate Cauchy distribution
A random vector$X=(X_{1},\ldots ,X_{k})^{T))$ is said to have the multivariate Cauchy distribution if every linear combination of its components $Y=a_{1}X_{1}+\cdots +a_{k}X_{k))$ has a Cauchy distribution. That is, for any constant vector $a\in \mathbb {R} ^{k))$, the random variable $Y=a^{T}X$ should have a univariate Cauchy distribution.^{[25]} The characteristic function of a multivariate Cauchy distribution is given by:
$\varphi _{X}(t)=e^{ix_{0}(t)-\gamma (t)},\!$
where $x_{0}(t)$ and $\gamma (t)$ are real functions with $x_{0}(t)$ a homogeneous function of degree one and $\gamma (t)$ a positive homogeneous function of degree one.^{[25]} More formally:^{[25]}
$x_{0}(at)=ax_{0}(t),$
$\gamma (at)=|a|\gamma (t),$
for all $t$.
An example of a bivariate Cauchy distribution can be given by:^{[26]}
Analogous to the univariate density, the multidimensional Cauchy density also relates to the multivariate Student distribution. They are equivalent when the degrees of freedom parameter is equal to one. The density of a $k$ dimension Student distribution with one degree of freedom becomes:
Properties and details for this density can be obtained by taking it as a particular case of the multivariate Student density.
Transformation properties
If $X\sim \operatorname {Cauchy} (x_{0},\gamma )$ then $kX+\ell \sim {\textrm {Cauchy))(x_{0}k+\ell ,\gamma |k|)$^{[27]}
If $X\sim \operatorname {Cauchy} (x_{0},\gamma _{0})$ and $Y\sim \operatorname {Cauchy} (x_{1},\gamma _{1})$ are independent, then $X+Y\sim \operatorname {Cauchy} (x_{0}+x_{1},\gamma _{0}+\gamma _{1})$ and $X-Y\sim \operatorname {Cauchy} (x_{0}-x_{1},\gamma _{0}+\gamma _{1})$
If $X\sim \operatorname {Cauchy} (0,\gamma )$ then ${\tfrac {1}{X))\sim \operatorname {Cauchy} (0,{\tfrac {1}{\gamma )))$
McCullagh's parametrization of the Cauchy distributions:^{[28]} Expressing a Cauchy distribution in terms of one complex parameter $\psi =x_{0}+i\gamma$, define $X\sim \operatorname {Cauchy} (\psi )$ to mean $X\sim \operatorname {Cauchy} (x_{0},|\gamma |)$. If $X\sim \operatorname {Cauchy} (\psi )$ then:
The Cauchy distribution is the stable distribution of index 1. The Lévy–Khintchine representation of such a stable distribution of parameter $\gamma$ is given, for $X\sim \operatorname {Stable} (\gamma ,0,0)\,$ by:
and $c_{1,\gamma },c_{2,\gamma ))$ can be expressed explicitly.^{[29]} In the case $\gamma =1$ of the Cauchy distribution, one has $c_{1,\gamma }=c_{2,\gamma ))$.
This last representation is a consequence of the formula
In spectroscopy, the Cauchy distribution describes the shape of spectral lines which are subject to homogeneous broadening in which all atoms interact in the same way with the frequency range contained in the line shape. Many mechanisms cause homogeneous broadening, most notably collision broadening.^{[30]}Lifetime or natural broadening also gives rise to a line shape described by the Cauchy distribution.
Applications of the Cauchy distribution or its transformation can be found in fields working with exponential growth. A 1958 paper by White ^{[31]} derived the test statistic for estimators of ${\hat {\beta ))$ for the equation $x_{t+1}=\beta {x}_{t}+\varepsilon _{t+1},\beta >1$ and where the maximum likelihood estimator is found using ordinary least squares showed the sampling distribution of the statistic is the Cauchy distribution.
Fitted cumulative Cauchy distribution to maximum one-day rainfalls using CumFreq, see also distribution fitting^{[13]}
The Cauchy distribution is often the distribution of observations for objects that are spinning. The classic reference for this is called the Gull's lighthouse problem^{[32]} and as in the above section as the Breit–Wigner distribution in particle physics.
In hydrology the Cauchy distribution is applied to extreme events such as annual maximum one-day rainfalls and river discharges. The blue picture illustrates an example of fitting the Cauchy distribution to ranked monthly maximum one-day rainfalls showing also the 90% confidence belt based on the binomial distribution. The rainfall data are represented by plotting positions as part of the cumulative frequency analysis.
^Frederic, Chyzak; Nielsen, Frank (2019). "A closed-form formula for the Kullback-Leibler divergence between Cauchy distributions". arXiv:1905.10965. Bibcode:2019arXiv190510965C. ((cite journal)): Cite journal requires |journal= (help)
^Nielsen, Frank; Okamura, Kazuki (2021). "On $ f $-divergences between Cauchy distributions". arXiv:2101.12459. ((cite journal)): Cite journal requires |journal= (help)
^ ^{a}^{b}^{c}CumFreq, free software for cumulative frequency analysis and probability distribution fitting [1]Archived 2018-02-21 at the Wayback Machine
^Cane, Gwenda J. (1974). "Linear Estimation of Parameters of the Cauchy Distribution Based on Sample Quantiles". Journal of the American Statistical Association. 69 (345): 243–245. doi:10.1080/01621459.1974.10480163. JSTOR2285535.
^Zhang, Jin (2010). "A Highly Efficient L-estimator for the Location Parameter of the Cauchy Distribution". Computational Statistics. 25 (1): 97–105. doi:10.1007/s00180-009-0163-y. S2CID123586208.
^ ^{a}^{b}Rothenberg, Thomas J.; Fisher, Franklin, M.; Tilanus, C.B. (1964). "A note on estimation from a Cauchy sample". Journal of the American Statistical Association. 59 (306): 460–463. doi:10.1080/01621459.1964.10482170.
^Barnett, V. D. (1966). "Order Statistics Estimators of the Location of the Cauchy Distribution". Journal of the American Statistical Association. 61 (316): 1205–1218. doi:10.1080/01621459.1966.10482205. JSTOR2283210.