[SciPy-User] R: Re: Epanechnikov kernel
Sat Jan 19 08:21:47 CST 2013
On Sat, Jan 19, 2013 at 8:48 AM, email@example.com
> is there a possibility to multivariate KDE using Epanechnikov kernel? my
> variables are X Y (point position)
As Josef mentioned there is no way for the user to choose the kernel
at present. The functionality is there, but it needs to be hooked in
with a suitable API. I didn't keep up with these discussions, so I
don't know the current status. If it's something you're interested in
trying to help with, I'm sure people would be appreciative and you can
ping the statsmodels mailing list.
Practically though, the reason this hasn't been done yet is that the
choice of the kernel is not all that important. Bandwidth selection is
the most important variable and other kernels perform similarly given
a good bandwidth. Is there any particular reason you want Epanechnikov
kernel in particular?
>>Data: 19/01/2013 14.32
>>A: "SciPy Users List"<firstname.lastname@example.org>
>>Ogg: Re: [SciPy-User] Epanechnikov kernel
>>On Sat, Jan 19, 2013 at 7:49 AM, <email@example.com> wrote:
>>> On Sat, Jan 19, 2013 at 6:34 AM, firstname.lastname@example.org
>>> <email@example.com> wrote:
>>>> Hi all,
>>>> I have a question for you. Is it possible in scipy using a Epanechnikov
>>>> kernel function?
>>>> I checked on scipy documentation but i found that the only way to
>>>> kernel-density estimate is possible only with using Gaussian kernels?
>>>> Is it true?
>>> Yes, kde in scipy.stats only has gaussian_kde
>>> Also in statsmodels currently only gaussian is supported for
>>> continuous data
>>> (It was removed because in the references only the bandwidth selection
>>> made much difference in the estimation, but not the shape of the
>>> kernel. Other kernels for continuous variables will come back
>>If you're interested in univariate KDE, then we do have the Epanechnikov
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