Tue Mar 30 16:19:43 CDT 2010
On Tue, Mar 30, 2010 at 5:04 PM, Dan bole <firstname.lastname@example.org> wrote:
> Hi all,
> I am trying to create a series of random variables selected from a
> distribution. I would like this distribution to start as a normal
> distribution, but then be altered based on assumptions of skewness and
> kurtosis (so I am not calculating skewness/kurtosis from a dataset, but
> instead creating the probability density function from assumptions of
> skewness/kurtosis). I can create a normal distribution and then pull random
> variables from this, and was wondering if it is possible to create a
> distribution based on assumptions of skewness and kurtosis?
> Many thanks,
For creating random variables, I would look into pymc, I remember it
has a skew-normal, but I don't know if it also has a distribution for
A warning: don't use scipy.stats.pdf_approx and friends for estimation
because they don't produce correct results.
I wrote a Gram-Charlier normal expansion function, but I don't
remember if it only does estimation or has all distribution methods.
If I did an rvs method, then it would be only generic and possibly
If you are interested, then I can dig up my scripts.
When I googled this last year, Gram-Charlier expansion was the best
search term for this.
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