In probability theory, the slash distribution is the probability distribution of a standard normal variate divided by an independent standard uniform variate. In other words, if the random variable Z has a normal distribution with zero mean and unit variance, the random variable U has a uniform distribution on [0,1] and Z and U are statistically independent, then the random variable X = Z / U has a slash distribution. The slash distribution is an example of a ratio distribution. The distribution was named by William H. Rogers and John Tukey in a paper published in 1972.
Probability density function
Cumulative distribution function
|Mean||Does not exist|
|Variance||Does not exist|
|Skewness||Does not exist|
|Ex. kurtosis||Does not exist|
|MGF||Does not exist|
The probability density function (pdf) is
where is the probability density function of the standard normal distribution. The result is undefined at x = 0, but the discontinuity is removable:
The most common use of the slash distribution is in simulation studies. It is a useful distribution in this context because it has heavier tails than a normal distribution, but it is not as pathological as the Cauchy distribution.
- Davison, Anthony Christopher; Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge University Press. p. 484. ISBN 978-0-521-57471-6. Retrieved 24 September 2012.
- Rogers, W. H.; Tukey, J. W. (1972). "Understanding some long-tailed symmetrical distributions". Statistica Neerlandica. 26 (3): 211–226. doi:10.1111/j.1467-9574.1972.tb00191.x.
- "SLAPDF". Statistical Engineering Division, National Institute of Science and Technology. Retrieved 2009-07-02.