[R] Slightly off-topic --- distribution name.

David Scott d.scott at auckland.ac.nz
Thu Sep 16 00:00:26 CEST 2004

I believe this is the skew-Laplace distribution, although the skew-Laplace 
does allow for the location of the mode of the distribution to vary.

Have a look at the function dskewlap in HyperbolicDist. The help on that 
function gives a reference to a paper by Feiller et al which describes the 

David Scott

On Wed, 15 Sep 2004, Rolf Turner wrote:

> I've built R functions to ``effect'' a particular distribution, and
> would like to find out if that distribution is already ``known'' by
> an existing name.  (I.e. suppose it were called the ``Melvin''
> distribution --- I've built dmelvin, pmelvin, qmelvin, and rmelvin as
> it were, but I need a real name to substitute for melvin.)
> The distribution is really just a toy --- but it provides a nice (and
> ``non-obviouse'') example of a two parameter distribution where both
> the moment and maximum likelihood equations for the parameter
> estimators are readily solvable, but at the same time are
> ``interesting''.  So it's good for exercises in an intro math-stats
> course.
> The distribution is simply that of the ***difference*** of two
> independent exponential variates, with different parameters.
> I.e.  X = U - V  where U ~ exp(beta) and V ~ exp(alpha) (where
> E(U) = beta, E(V) = alpha).
> This makes the distribution of X something like an asymetric Laplace
> distribution, with its mode at 0.  (One could shift the mode too, but
> that would add a third parameter, which would be de trop.)
> Anyhow:  Is this a ``known'' distribution?  Does it have a name?
> (I've never seen it mentioned in any of the intro math-stat books
> that I've looked into.) If not, can anyone suggest a good name for
> it?  (Don't be rude now!)
> 				cheers,
> 					Rolf Turner
> 					rolf at math.unb.ca
> P. S.  To save you putting pen to paper and working it out,
>       the density function is
>               { exp(x/alpha)/(alpha + beta) for x <= 0
> 	f(x) = {
>               { exp(-x/beta)/(alpha + beta) for x >= 0
>       The mean and variance are mu = beta - alpha and
>       sigma^2 = alpha^2 + beta^2 respectfully. :-)
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David Scott	Department of Statistics, Tamaki Campus
 		The University of Auckland, PB 92019
 		Auckland	NEW ZEALAND
Phone: +64 9 373 7599 ext 86830		Fax: +64 9 373 7000
Email:	d.scott at auckland.ac.nz

Graduate Officer, Department of Statistics

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