In probability theory and statistics, the asymmetric Laplace distribution is a continuous probability distribution which is a generalization of the Laplace distribution. Just as the Laplace distribution consists of two exponential distributions of equal scale back-to-back about x = m, the asymmetric Laplace consists of two exponential distributions of unequal scale back to back about x = m, adjusted to assure continuity and normalization. The difference of two variates exponentially distributed with different means and rate parameters will be distributed according to the ALD. When the two rate parameters are equal, the difference will be distributed according to the Laplace distribution.
The ALD characteristic function is given by: For m = 0, the ALD is a member of the family of geometric stable distributions with α = 2. It follows that if and are two distinct ALD characteristic functions with m = 0, then is also an ALD characteristic function with location parameter. The new scale parameter λ obeys and the new skewness parameter κ obeys:
Moments, mean, variance, skewness
The n-th moment of the ALD about m is given by From the binomial theorem, the n-th moment about zero is then: where is the generalized exponential integral function The first moment about zero is the mean: The variance is: and the skewness is:
Generating asymmetric Laplace variates
Asymmetric Laplace variates may be generated from a random variateU drawn from the uniform distribution in the interval by: where s=sgn. They may also be generated as the difference of two exponential distributions. If X1 is drawn from exponential distribution with mean and rate and X2 is drawn from an exponential distribution with mean and rate then X1 - X2 is distributed according to the asymmetric Laplace distribution with parameters
An alternative parametrization is made possible by the characteristic function: where is a location parameter, is a scale parameter, is an asymmetry parameter. This is specified in Section 2.6.1 and Section 3.1 of Lihn. Its probability density function is where and. It follows that. The n-th moment about is given by The mean about zero is:The variance is:The skewness is:The excess kurtosis is:For small, the skewness is about . Thus represents skewness in an almost direct way.