[SciPy-Dev] Expanding Scipy's KDE functionality

[Sturla Molden]

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.

Sturla