[SciPy-Dev] scipy.stats.kde

[Sam Birch]

Well perhaps I should start with a module that does other kernels &
bandwidth estimation then? Then everybody who uses them can use a standard
implementation. Is that an appropriate addition to SciPy core?

On Fri, Aug 27, 2010 at 2:38 PM, <josef.pktd at gmail.com> wrote:

> On Fri, Aug 27, 2010 at 2:17 PM, Sam Birch <sam.m.birch at gmail.com> wrote:
> > Hi all,
> > I was thinking of renovating the kernel density estimation package
> (although
> > no promises; I'm leaving for college tomorrow morning!). I was wondering:
> > a) whether anyone had started code in that direction
>
> Mike Crowe wrote code for kernel regression  and Skipper started a 1D
> kernel density estimator in scikits.statsmodels, which cover a larger
> number of kernels
>
> I don't think I have seen any higher dimensional kernel density
> estimation in python besides scipy.stats.kde. The Gaussian kde in
> scipy.stats is targeted to the underlying Fortran code for
> multivariate normal cdf.
> It's not clear to me what other n-dimensional kdes would require or
> whether they would fit well with the current code.
>
> One extension that Robert also mentioned in the past that it would be
> nice to have adaptive kernels, which I also haven't seen in python
> yet.
>
> > b) what people want in it
> > I was thinking (as an ideal, not necessarily goal):
> > - Support for more than Gaussian kernels (e.g. custom,
> > uniform, Epanechnikov, triangular, quartic, cosine, etc.)
> > - More options for bandwidth selection (custom bandwidth matrices, AMISE
> > optimization, cross-validation, etc.)
>
> definitely yes, I don't think they are even available for 1D yet.
>
> > - Assorted conveniences: automatically generate the mesh, limit the
> kernel's
> > support for speed
>
> Using scipy.spatial to limit the number of neighbors in a bounded
> support kernel might be a good idea.
>
> (just some thought on the topic)
>
> Josef
>
> > So, thoughts anyone? I figure it's better to over-specify and then
> > under-produce, so don't hold back.
> > Thanks,
> > Sam
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