[Scipy-tickets] [SciPy] #422: exponweib.stats
SciPy
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Thu May 17 15:21:18 CDT 2007
#422: exponweib.stats
-------------------------+--------------------------------------------------
Reporter: dhuard | Owner: somebody
Type: defect | Status: new
Priority: normal | Milestone:
Component: scipy.stats | Version:
Severity: normal | Keywords:
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exponweib.stats(2,3) returns
{{{
exceptions.ValueError Traceback (most
recent call last)
/home/huardda/science_svn/pymc/trial/PyMC2/<ipython console>
/usr/local/lib/python2.4/site-packages/scipy/stats/distributions.py in
stats(self, *args, **kwds)
644 if 'm' in moments:
645 if mu is None:
--> 646 mu = self._munp(1.0,*goodargs)
647 out0 = default.copy()
648 place(out0,cond,mu*scale+loc)
/usr/local/lib/python2.4/site-packages/scipy/stats/distributions.py in
_munp(self, n, *args)
387 # Central moments
388 def _munp(self,n,*args):
--> 389 return self.generic_moment(n,*args)
390
391 def __fix_loc_scale(self, args, loc, scale):
/usr/local/lib/python2.4/site-packages/numpy/lib/function_base.py in
__call__(self, *args)
898 if self.nin:
899 if (nargs > self.nin) or (nargs <
self.nin_wo_defaults):
--> 900 raise ValueError, "mismatch between python
function inputs"\
901 " and received arguments"
902
ValueError: mismatch between python function inputs and received arguments
}}}
I found a paper with a direct formula for the moments. Would that be
useful ? Here is the reference :
{{{
TY - JOUR
JF - Metrika
T1 - A Simple Derivation of Moments of the Exponentiated Weibull
Distribution
VL - 62
IS - 1
SP - 17
EP - 22
PY - 2005/09/01/
UR - http://dx.doi.org/10.1007/s001840400351
M3 - 10.1007/s001840400351
AU - Choudhury, Amit
N2 - The Exponentiated Weibull family is an extension of the Weibull
family obtained by adding an additional shape parameter. The beauty and
importance of this distribution lies in its ability to model monotone as
well as non-monotone failure rates which are quite common in reliability
and biological studies. As with any other distribution, many of its
interesting characteristics and features can be studied through moments.
Presently, moments of this distribution are available only under certain
restrictions. In this paper, a general derivation of moments without any
restriction whatsoever is proposed. A compact expression for moments is
presented.
}}}
--
Ticket URL: <http://projects.scipy.org/scipy/scipy/ticket/422>
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