[SciPy-user] guassian_kde and kernel regression
Anne Archibald
aarchiba@physics.mcgill...
Sat Nov 1 18:51:08 CDT 2008
2008/11/1 Frank Lagor <dfranci@seas.upenn.edu>:
> This question is probably for Robert Kern, because I believe the he wrote
> the gaussian_kde class in scipy.stats.kde, however I would very much
> appreciate a response from anyone else who could help. My question is: Is
> there currently any way to perform weighted kernel density estimation using
> the gaussian_kde class? If not, what needs to be done, and how do I get
> started?
>
> Just for clarity sake-- by weighted KDE I mean that I have more than just
> the distribution of points for the density estimate. I also have an
> associated probability with each point. In this case, I believe it becomes
> a regression problem and I think is referred to as kernel regression. I
> would very much like to use the class to perform both KDE and wKDE.
The class does not support weights right now, but I don't think it
would be very difficult to add them to most parts of the code,
essentially just adding a "weights" optional argument. The automatic
covariance selection would need some rethinking; you'd need to hunt
down some research papers. (That method is only really appropriate for
unimodal distributions anyway.) But it does seem valuable.
Anne
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