[SciPy-Dev] scipy.stats.distributions: note on initial parameters for fitting the beta distribution
Mon Nov 1 22:13:51 CDT 2010
On Sun, Oct 31, 2010 at 9:34 AM, James Phillips <email@example.com> wrote:
> File attached.
> On Sun, Oct 31, 2010 at 8:33 AM, James Phillips <firstname.lastname@example.org> wrote:
>> Here is a more polished and quite smaller version of your example file
>> matchdist.py that uses either nnlf or residuals for ranking, and
>> includes checks for NaN, +inf and -inf. I think this has all of the
>> logic and range checks that it needs.
I tried out both your scripts during the weekend but didn't get around
to replying. Most distributions work pretty fast but there are still a
few unsuccesful time wasters in there. For example, ksone is also
mainly a distribution for a statistical test, and in my run took a
long time without a successful fit.
I'm not sure how nnlf woill work as selection criterium for the
distributions, and similarly the residual sum of squares might not be
a good or robust criterium, for example with heavy tailed
distributions. But that's just a guess, the only (commercial) package
that I looked at, offered the choice between Kolmogorov-Smirnov,
Anderson-Darling and 2 chisquare tests (equal-spaced and equal
probability) as distance or goodness-of-fit measure. (Entropy would be
another criterium, but I haven't seen it yet for selecting the
Your version also will help to narrow down what might be good starting
values, eventually I would prefer to hardcode (optional) distribution
Your second script (after dropping the failures) has 10 distributions
fewer than the first script (70 instead of 80), so there is still some
distribution specific work left.
>> 2010/10/30 James Phillips <email@example.com>:
>>> I'll parallelize this code and make a few more tweaks, and then add it
>>> to my web site.
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