Wrapped Cauchy distribution

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Wrapped Cauchy
Probability density function

The support is chosen to be [-π,π)
Cumulative distribution function

The support is chosen to be [-π,π)
Parameters \mu Real
\gamma >0
Support -\pi \leq \theta <\pi
pdf {\frac {1}{2\pi }}\,{\frac {\sinh(\gamma )}{\cosh(\gamma )-\cos(\theta -\mu )}}
CDF \,
Mean \mu (circular)
Variance 1-e^{{-\gamma }} (circular)
Entropy \ln(2\pi (1-e^{{-2\gamma }})) (differential)
CF e^{{in\mu -|n|\gamma }}

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 Cauchy distribution is sometimes known as a Lorentzian distribution, and the wrapped Cauchy distribution may sometimes be referred to as a wrapped Lorentzian distribution.

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)

Description

The probability density function of the wrapped Cauchy distribution is:[1]

f_{{WC}}(\theta ;\mu ,\gamma )=\sum _{{n=-\infty }}^{\infty }{\frac {\gamma }{\pi (\gamma ^{2}+(\theta -\mu +2\pi n)^{2})}}

where \gamma is the scale factor and \mu is the peak position of the "unwrapped" distribution. Expressing the above pdf in terms of the characteristic function of the Cauchy distribution yields:

f_{{WC}}(\theta ;\mu ,\gamma )={\frac {1}{2\pi }}\sum _{{n=-\infty }}^{\infty }e^{{in(\theta -\mu )-|n|\gamma }}={\frac {1}{2\pi }}\,\,{\frac {\sinh \gamma }{\cosh \gamma -\cos(\theta -\mu )}}

In terms of the circular variable z=e^{{i\theta }} the circular moments of the wrapped Cauchy distribution are the characteristic function of the Cauchy distribution evaluated at integer arguments:

\langle z^{n}\rangle =\int _{\Gamma }e^{{in\theta }}\,f_{{WC}}(\theta ;\mu ,\gamma )\,d\theta =e^{{in\mu -|n|\gamma }}.

where \Gamma \, is some interval of length 2\pi . The first moment is then the average value of z, also known as the mean resultant, or mean resultant vector:

\langle z\rangle =e^{{i\mu -\gamma }}

The mean angle is

\langle \theta \rangle ={\mathrm {Arg}}\langle z\rangle =\mu

and the length of the mean resultant is

R=|\langle z\rangle |=e^{{-\gamma }}

Estimation of parameters

A series of N measurements z_{n}=e^{{i\theta _{n}}} drawn from a wrapped Cauchy distribution may be used to estimate certain parameters of the distribution. The average of the series \overline {z} is defined as

\overline {z}={\frac {1}{N}}\sum _{{n=1}}^{N}z_{n}

and its expectation value will be just the first moment:

\langle \overline {z}\rangle =e^{{i\mu -\gamma }}

In other words, \overline {z} is an unbiased estimator of the first moment. If we assume that the peak position \mu lies in the interval [-\pi ,\pi ), then Arg(\overline {z}) will be a (biased) estimator of the peak position \mu .

Viewing the z_{n} as a set of vectors in the complex plane, the \overline {R}^{2} statistic is the length of the averaged vector:

\overline {R}^{2}=\overline {z}\,\overline {z^{*}}=\left({\frac {1}{N}}\sum _{{n=1}}^{N}\cos \theta _{n}\right)^{2}+\left({\frac {1}{N}}\sum _{{n=1}}^{N}\sin \theta _{n}\right)^{2}

and its expectation value is

\langle \overline {R}^{2}\rangle ={\frac {1}{N}}+{\frac {N-1}{N}}e^{{-2\gamma }}.

In other words, the statistic

R_{e}^{2}={\frac {N}{N-1}}\left(\overline {R}^{2}-{\frac {1}{N}}\right)

will be an unbiased estimator of e^{{-2\gamma }}, and \ln(1/R_{e}^{2})/2 will be a (biased) estimator of \gamma .

Entropy

The information entropy of the wrapped Cauchy distribution is defined as:[1]

H=-\int _{\Gamma }f_{{WC}}(\theta ;\mu ,\gamma )\,\ln(f_{{WC}}(\theta ;\mu ,\gamma ))\,d\theta

where \Gamma is any interval of length 2\pi . The logarithm of the density of the wrapped Cauchy distribution may be written as a Fourier series in \theta \,:

\ln(f_{{WC}}(\theta ;\mu ,\gamma ))=c_{0}+2\sum _{{m=1}}^{\infty }c_{m}\cos(m\theta )

where

c_{m}={\frac {1}{2\pi }}\int _{\Gamma }\ln \left({\frac {\sinh \gamma }{2\pi (\cosh \gamma -\cos \theta )}}\right)\cos(m\theta )\,d\theta

which yields:

c_{0}=\ln \left({\frac {1-e^{{-2\gamma }}}{2\pi }}\right)

(c.f. Gradshteyn and Ryzhik [2] 4.224.15) and

c_{m}={\frac {e^{{-m\gamma }}}{m}}\qquad {\mathrm {for}}\,m>0

(c.f. Gradshteyn and Ryzhik [2] 4.397.6). The characteristic function representation for the wrapped Cauchy distribution in the left side of the integral is:

f_{{WC}}(\theta ;\mu ,\gamma )={\frac {1}{2\pi }}\left(1+2\sum _{{n=1}}^{\infty }\phi _{n}\cos(n\theta )\right)

where \phi _{n}=e^{{-|n|\gamma }}. 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:

H=-c_{0}-2\sum _{{m=1}}^{\infty }\phi _{m}c_{m}=-\ln \left({\frac {1-e^{{-2\gamma }}}{2\pi }}\right)-2\sum _{{m=1}}^{\infty }{\frac {e^{{-2n\gamma }}}{n}}

The series is just the Taylor expansion for the logarithm of (1-e^{{-2\gamma }}) so the entropy may be written in closed form as:

H=\ln(2\pi (1-e^{{-2\gamma }}))\,

See also

References

  1. ↑ 1.0 1.1 Mardia, Kantilal; Jupp, Peter E. (1999). Directional Statistics. Wiley. ISBN 978-0-471-95333-3. Retrieved 2011-07-19. 
  2. ↑ 2.0 2.1 Gradshteyn, I.; Ryzhik, I. (2007). Jeffrey, Alan; Zwillinger, Daniel, eds. Table Of Integrals, Series And Products (7 ed.). Academic Press. ISBN 0-12-373637-4. 
 
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