Wrapped Lévy distribution

In probability theory and directional statistics, a wrapped Lévy distribution is a wrapped probability distribution that results from the "wrapping" of the Lévy distribution around the unit circle.

Description

The pdf of the wrapped Lévy distribution is

f_{WL}(\theta ;\mu ,c)=\sum _{n=-\infty }^{\infty }{\sqrt {\frac {c}{2\pi }}}\,{\frac {e^{-c/2(\theta +2\pi n-\mu )}}{(\theta +2\pi n-\mu )^{3/2}}}

where the value of the summand is taken to be zero when $\theta +2\pi n-\mu \leq 0$ , $c$ is the scale factor and $\mu$ is the location parameter. Expressing the above pdf in terms of the characteristic function of the Lévy distribution yields:

f_{WL}(\theta ;\mu ,c)={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }e^{-in(\theta -\mu )-{\sqrt {c|n|}}\,(1-i\operatorname {sgn} {n})}={\frac {1}{2\pi }}\left(1+2\sum _{n=1}^{\infty }e^{-{\sqrt {cn}}}\cos \left(n(\theta -\mu )-{\sqrt {cn}}\,\right)\right)

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

\langle z^{n}\rangle =\int _{\Gamma }e^{in\theta }\,f_{WL}(\theta ;\mu ,c)\,d\theta =e^{in\mu -{\sqrt {c|n|}}\,(1-i\operatorname {sgn}(n))}.

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

\langle z\rangle =e^{i\mu -{\sqrt {c}}(1-i)}

The mean angle is

\theta _{\mu }=\mathrm {Arg} \langle z\rangle =\mu +{\sqrt {c}}

and the length of the mean resultant is

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

References

Fisher, N. I. (1996). Statistical Analysis of Circular Data. Cambridge University Press. ISBN 978-0-521-56890-6. Retrieved 2010-02-09.

Probability distributions (list)

Discrete
univariate

with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric negative Poisson binomial Rademacher soliton discrete uniform Zipf Zipf–Mandelbrot
with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Flory–Schulz Gauss–Kuzmin geometric logarithmic mixed Poisson negative binomial Panjer parabolic fractal Poisson Skellam Yule–Simon zeta

Continuous
univariate

supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular continuous Bernoulli Irwin–Hall Kumaraswamy logit-normal noncentral beta PERT raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle
supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi chi-squared noncentral inverse scaled Dagum Davis Erlang hyper exponential hyperexponential hypoexponential logarithmic F noncentral folded normal Fréchet gamma generalized inverse gamma/Gompertz Gompertz shifted half-logistic half-normal Hotelling's T-squared inverse Gaussian generalized Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal log-t Lomax matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami Pareto phase-type Poly-Weibull Rayleigh relativistic Breit–Wigner Rice truncated normal type-2 Gumbel Weibull discrete Wilks's lambda
supported on the whole real line	Cauchy exponential power Fisher's z Kaniadakis κ-Gaussian Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's S_U Landau Laplace asymmetric logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t Tracy–Widom variance-gamma Voigt
with support whose type varies	generalized chi-squared generalized extreme value generalized Pareto Marchenko–Pastur Kaniadakis κ-exponential Kaniadakis κ-Gamma Kaniadakis κ-Weibull Kaniadakis κ-Logistic Kaniadakis κ-Erlang q-exponential q-Gaussian q-Weibull shifted log-logistic Tukey lambda