[R] Non-parametric regression

[Ralf B]

I have two data sets, each a vector of 1000 numbers, each vector
representing a distribution (i.e. 1000 numbers each of which
representing a frequency at one point on a scale between 1 and 1000).
For similfication, here an short version with only 5 points.


a <- c(8,10,8,12,4)
b <- c(7,11,8,10,5)

Leaving the obvious discussion about causality aside fro a moment, I
would like to see how well i can predict b from a using a regression.
Since I do not know anything about the distribution type and already
discovered non-normality I cannot use parametric regression or
anything GLM for that matter.

How should I proceed in using non-parametric regression to model
vector a and see how well it predicts b? Perhaps you could extend the
given lines into a short example script to give me an idea? Are there
any other options?

Best,
Ralf