Wrapped exponential distribution

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

The probability density function of the wrapped exponential distribution is^[1]

${\displaystyle f_{\text{WE}}(\theta ;\lambda )=\sum _{k=0}^{\infty }\lambda e^{-\lambda (\theta +2\pi k)}={\frac {\lambda e^{-\lambda \theta }}{1-e^{-2\pi \lambda }}},}${\displaystyle f_{\text{WE}}(\theta ;\lambda )=\sum _{k=0}^{\infty }\lambda e^{-\lambda (\theta +2\pi k)}={\frac {\lambda e^{-\lambda \theta }}{1-e^{-2\pi \lambda }}},}

for ${\displaystyle 0\leq \theta <2\pi }${\displaystyle 0\leq \theta <2\pi } where ${\displaystyle \lambda >0}${\displaystyle \lambda >0} is the rate parameter of the unwrapped distribution. This is identical to the truncated distribution obtained by restricting observed values X from the exponential distribution with rate parameter λ to the range ${\displaystyle 0\leq X<2\pi }${\displaystyle 0\leq X<2\pi }. Note that this distribution is not periodic.

The characteristic function of the wrapped exponential is just the characteristic function of the exponential function evaluated at integer arguments:

${\displaystyle \varphi _{n}(\lambda )={\frac {1}{1-in/\lambda }}}${\displaystyle \varphi _{n}(\lambda )={\frac {1}{1-in/\lambda }}}

which yields an alternate expression for the wrapped exponential PDF in terms of the circular variable z = e^i(θ-m) valid for all real θ and m:

${\displaystyle {\begin{aligned}f_{\text{WE}}(z;\lambda )&={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }{\frac {z^{-n}}{1-in/\lambda }}\\[10pt]&={\begin{cases}{\frac {\lambda }{\pi }},円{\textrm {Im}}(\Phi (z,1,-i\lambda ))-{\frac {1}{2\pi }}&{\text{if }}z\neq 1\\[12pt]{\frac {\lambda }{1-e^{-2\pi \lambda }}}&{\text{if }}z=1\end{cases}}\end{aligned}}}${\displaystyle {\begin{aligned}f_{\text{WE}}(z;\lambda )&={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }{\frac {z^{-n}}{1-in/\lambda }}\\[10pt]&={\begin{cases}{\frac {\lambda }{\pi }},円{\textrm {Im}}(\Phi (z,1,-i\lambda ))-{\frac {1}{2\pi }}&{\text{if }}z\neq 1\\[12pt]{\frac {\lambda }{1-e^{-2\pi \lambda }}}&{\text{if }}z=1\end{cases}}\end{aligned}}}

where ${\displaystyle \Phi ()}${\displaystyle \Phi ()} is the Lerch transcendent function.

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

${\displaystyle \langle z^{n}\rangle =\int _{\Gamma }e^{in\theta },円f_{\text{WE}}(\theta ;\lambda ),円d\theta ={\frac {1}{1-in/\lambda }},}${\displaystyle \langle z^{n}\rangle =\int _{\Gamma }e^{in\theta },円f_{\text{WE}}(\theta ;\lambda ),円d\theta ={\frac {1}{1-in/\lambda }},}

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

${\displaystyle \langle z\rangle ={\frac {1}{1-i/\lambda }}.}${\displaystyle \langle z\rangle ={\frac {1}{1-i/\lambda }}.}

The mean angle is

${\displaystyle \langle \theta \rangle =\mathrm {Arg} \langle z\rangle =\arctan(1/\lambda ),}${\displaystyle \langle \theta \rangle =\mathrm {Arg} \langle z\rangle =\arctan(1/\lambda ),}

and the length of the mean resultant is

${\displaystyle R=|\langle z\rangle |={\frac {\lambda }{\sqrt {1+\lambda ^{2}}}}.}${\displaystyle R=|\langle z\rangle |={\frac {\lambda }{\sqrt {1+\lambda ^{2}}}}.}

and the variance is then 1 − R.

The wrapped exponential distribution is the maximum entropy probability distribution for distributions restricted to the range ${\displaystyle 0\leq \theta <2\pi }${\displaystyle 0\leq \theta <2\pi } for a fixed value of the expectation ${\displaystyle \operatorname {E} (\theta )}${\displaystyle \operatorname {E} (\theta )}.^[1]

• Wrapped distribution
• Directional statistics

• Continuous distributions
• Directional statistics

• Articles with short description
• Short description matches Wikidata