[SciPy-User] MLE with stats.lognorm

josef.pktd@gmai... josef.pktd@gmai...
Mon Oct 10 14:22:42 CDT 2011


On Mon, Oct 10, 2011 at 10:26 AM, Christian K. <ckkart@hoc.net> wrote:
>> >> for example with starting value for loc
>> >>>>> print stats.lognorm.fit(x, loc=0)
>> >> (0.23800805074491538, 0.034900026034516723, 196.31113801786194)
>> >
>> > I see. Is there any workaround/patch to force loc=0.0? What is the
>> > meaning of loc anyway?
>>
>> loc is the starting value for fmin, I don't remember how to specify
>> starting values for shape parameters, I never used it.
>>
>> As in the ticket you could monkey patch the _fitstart function
>>
>> >>> stats.cauchy._fitstart = lambda x:(0,1)
>> >>> stats.cauchy.fit(x)
>>
>> or what I do to experiment with starting values is
>>
>> stats.distributions.lognorm_gen._fitstart = fitstart_lognormal
>
> Ok, but this is not different from calling fit like
> stats.lognorm.fit(samples, loc=0.0)
>
> I would really need to force loc=0.0
>
> stats.lognorm.fit(samples, loc=0.0, floc=0.0)
>
> does not work either.

ok, I misunderstood that you want to fix the location parameter at zero

This looks like a different bug.

floc=0 doesn't seem to work, I don't get any results that look close
to the true values
With a sample size of 2000 the MLE should be pretty close to the true
parameters:


import numpy as np

from scipy import stats

np.set_printoptions(precision=4)
print 'true'
print 0.25, 0., 20.0
print 'estimated, floc=0, loc=0'
for i in range(10):
    x = stats.lognorm.rvs(0.25, 0., 20.0, size=2000)
    print np.array(stats.lognorm.fit(x, floc=0)), \
            np.array(stats.lognorm.fit(x, loc=0))

true
0.25 0.0 20.0
estimated, floc=0, loc=0
[ 2.1271  0.      2.3999] [  0.2623   1.0211  18.7911]
[ 2.1393  0.      2.3952] [  0.2523   0.0294  20.0117]
[ 2.1356  0.      2.3978] [  0.2477   0.03    19.9703]
[ 2.1378  0.      2.3874] [  0.2496   0.0301  19.9231]
[ 2.1463  0.      2.3641] [  0.2474   0.0292  19.9051]
[ 2.1408  0.      2.3898] [  0.2459   0.0303  20.0118]
[ 2.1252  0.      2.4326] [  0.251    0.029   20.0412]
[ 2.1296  0.      2.3943] [  0.2476   0.0296  19.8208]
[ 2.1344  0.      2.401 ] [  0.2472   0.0299  19.9744]
[ 2.1383  0.      2.4133] [  0.247    0.0301  20.1544]

floc=0 is supposed to fix the location at 0, loc=0 only provides a
starting value for loc, but still estimates loc

>
> Btw., I think the extradoc is quite misleading:

I think this might be just the non-standard parameterization of the
log-normal distribution because we use generic loc and scale handling.
The parameterization has been discussed in the mailing list and for
example in http://projects.scipy.org/scipy/ticket/1502

clearer documentation for this or a reparameterized distribution would
be helpful for lognorm

Josef

>
> """
> lognorm.pdf(x,s) = 1/(s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2)
> for x > 0, s > 0.
>
> If log x is normally distributed with mean mu and variance sigma**2,
> then x is log-normally distributed with shape paramter sigma and scale
> parameter exp(mu).
> """
>
> sigma seems to equal s in the function definition but mu does not appear at
> all. It seems to enter via _pdf()/scale when looking at distributions.py,
> wehere scale = exp(mu)?
>
> Christian
>
>
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