Log-Cauchy distribution

Log-Cauchy

Probability density function
Cumulative distribution function
Parameters	${\displaystyle \mu }$ (real) ${\displaystyle \displaystyle \sigma >0\!}$ (real)
Support	${\displaystyle \displaystyle x\in (0,+\infty )\!}$
PDF	${\displaystyle {1 \over x\pi }\left[{\sigma \over (\ln x-\mu )^{2}+\sigma ^{2}}\right],\ \ x>0}$
CDF	${\displaystyle {\frac {1}{\pi }}\arctan \left({\frac {\ln x-\mu }{\sigma }}\right)+{\frac {1}{2}},\ \ x>0}$
Mean	infinite
Median	${\displaystyle e^{\mu }\,}$
Variance	infinite
Skewness	does not exist
Excess kurtosis	does not exist
MGF	does not exist

In probability theory, a log-Cauchy distribution is a probability distribution of a random variable whose logarithm is distributed in accordance with a Cauchy distribution. If X is a random variable with a Cauchy distribution, then Y = exp(X) has a log-Cauchy distribution; likewise, if Y has a log-Cauchy distribution, then X = log(Y) has a Cauchy distribution.^[1]