In probability theory and directional statistics, a wrapped Cauchy distribution is a wrapped probability distribution that results from the "wrapping" of the Cauchy distribution around the unit circle.
The wrapped Cauchy distribution is often found in the field of spectroscopy where it is used to analyze diffraction patterns (e.g. see Fabry–Pérot interferometer).
The probability density function of the wrapped Cauchy distribution is:[1] where
is the peak position of the "unwrapped" distribution.
Expressing the above pdf in terms of the characteristic function of the Cauchy distribution yields: The PDF may also be expressed in terms of the circular variable z = eiθ and the complex parameter ζ = ei(μ+iγ) where, as shown below, ζ = ⟨z⟩.
the circular moments of the wrapped Cauchy distribution are the characteristic function of the Cauchy distribution evaluated at integer arguments: where
The first moment is then the average value of z, also known as the mean resultant, or mean resultant vector: The mean angle is and the length of the mean resultant is yielding a circular variance of 1 − R. A series of N measurements
is defined as and its expectation value will be just the first moment: In other words,
will be a (biased) estimator of the peak position
as a set of vectors in the complex plane, the
statistic is the length of the averaged vector: and its expectation value is In other words, the statistic will be an unbiased estimator of
The information entropy of the wrapped Cauchy distribution is defined as:[1] where
The logarithm of the density of the wrapped Cauchy distribution may be written as a Fourier series in
The characteristic function representation for the wrapped Cauchy distribution in the left side of the integral is: where
Substituting these expressions into the entropy integral, exchanging the order of integration and summation, and using the orthogonality of the cosines, the entropy may be written: The series is just the Taylor expansion for the logarithm of
so the entropy may be written in closed form as: If X is Cauchy distributed with median μ and scale parameter γ, then the complex variable has unit modulus and is distributed on the unit circle with density:[3] where and ψ expresses the two parameters of the associated linear Cauchy distribution for x as a complex number: It can be seen that the circular Cauchy distribution has the same functional form as the wrapped Cauchy distribution in z and ζ (i.e. fWC(z,ζ)).
( θ ; μ , γ )
is called the circular Cauchy distribution[3][4] (also the complex Cauchy distribution[3]) with parameters μ and γ.
(See also McCullagh's parametrization of the Cauchy distributions and Poisson kernel for related concepts.)
The circular Cauchy distribution expressed in complex form has finite moments of all orders for integer n ≥ 1.
For |φ| < 1, the transformation is holomorphic on the unit disk, and the transformed variable U(Z, φ) is distributed as complex Cauchy with parameter U(ζ, φ).
Given a sample z1, ..., zn of size n > 2, the maximum-likelihood equation can be solved by a simple fixed-point iteration: starting with ζ(0) = 0.
The sequence of likelihood values is non-decreasing, and the solution is unique for samples containing at least three distinct values.
[5] The maximum-likelihood estimate for the median (
) of a real Cauchy sample is obtained by the inverse transformation: For n ≤ 4, closed-form expressions are known for
[6] The density of the maximum-likelihood estimator at t in the unit disk is necessarily of the form: where Formulae for p3 and p4 are available.