[SciPy-User] MLE with stats.lognorm

[josef.pktd at gmail.com]

On Sun, Oct 9, 2011 at 8:06 AM,  <josef.pktd at gmail.com> wrote:
> On Sun, Oct 9, 2011 at 7:51 AM, Christian K. <ckkart at hoc.net> wrote:
>> Hi,
>>
>> I wonder whether I am doing something wrong or if the following is to be
>> expected (using sciyp 0.9):
>>
>> In [38]: from scipy import stats
>>
>> In [39]: dist = stats.lognorm(0.25,scale=200.0)
>>
>> In [40]: samples = dist.rvs(size=100)
>>
>> In [41]: print stats.lognorm.fit(samples)
>> C:\Python26\lib\site-packages\scipy\optimize\optimize.py:280: RuntimeWarning:
>> invalid value encountered in subtract
>>  and max(abs(fsim[0]-fsim[1:])) <= ftol):
>> (1.0, 158.90310231282845, 21.013288720647015)
>>
>> In [42]: print stats.lognorm.fit(samples, floc=0)
>> [2.2059200167655884, 0, 21.013288720647015]
>>
>> Even when fixing loc=0.0, the results from the MLE for s and scale are very
>> different from the input parameters. Is lognorm
>>
>> Any hints are highly appreciated.
>
> I just looked at similar cases, for the changes in scipy 0.9 and
> starting values, see
> http://projects.scipy.org/scipy/ticket/1530
>
> Essentially, you need to find better starting values and give it to fit.
>
> Can you add it to the ticket? It's not quite the same, but I guess it
> is also that fix_loc_scale doesn't make sense.
>
> Note, I also get many of these warnings,
>
>> invalid value encountered in subtract
>>  and max(abs(fsim[0]-fsim[1:])) <= ftol):
>
> they are caused when np.inf is returned for invalid arguments. In many
> cases optimize.fmin evaluates parameters that are not valid, but most
> of the time that doesn't seem to cause any problems, exept it's
> annoying.

for example with starting value for loc
>>> print stats.lognorm.fit(x, loc=0)
(0.23800805074491538, 0.034900026034516723, 196.31113801786194)

Josef

>
> Josef
>
>
>>
>> Best regards, Christian
>>
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>