[SciPy-Dev] Expanding Scipy's KDE functionality
Fri Jan 25 07:53:09 CST 2013
On 25.01.2013 14:45, Sturla Molden wrote:
> One can always use a delta function as kernel though. It retains all the
> information we have about the sampled distribution.
This is not as crazy as it might sound. It the basis for bootstrap and
jack-knife procedures, PRESS in regression analysis, etc. Also by
viewing a data sample as an "analog signal" consisting of a sum of delta
functions (or equivalently: a KDE using a delta kernel), all the methods
of DSP becomes available to statistical data analysis. The first step in
which case is to digitize the signal by anti-alias filtering and regular
sampling. And as it turs out, the anti-alias filtering is just another
case of KDE.
More information about the SciPy-Dev