I'm an applied mathematician working on approximation theory and its applications to optics. I've been working with a Gaussian Kernel Density Estimator (KDE) for some while, but figured out I should be looking broader, and understand better what kernel I should be working with and how.
I'm looking for a shorter-then-text book introduction to KDE's, either as a short review, book chapter or even a slideshow. The main questions I need to be adressed:
- What is the main criteria for choosing a specific KDE method over the other?
- I understand the meaning of the "window size", but what are the main methods for choosing it?