You produce a non-parametric distribution. Then you obtain, say, 10 random variables (RV) from this non-parametric distribution- much the same way as you would obtain random variables from a (parametric) normal distribution with stated mean and variance. But unlike the parametric distribution, where our RVs would occur around the mean (our parameter), RVs from a non-parametric distribution occur *within *the range bound by the lowest and highest mass point. This was not necessarily an intuitive concept to me, when I first stumbled across it. Which is why this mathematical proof of this range made me feel so much more comfortable:

If our estimate of the RV is a simple weighted-mean of the mass points:

Furthermore, since for RV :

Since , we can express the inequality as:

On the other hand, If we know further information, like individual weights:

Furthermore, since for intercept :

Since , we can express the inequality as:

Thus, it is proven that any estimates of an RV drawn from a non-parametric distribution will be bound by the highest and lowest mass point.

Abbas Keshvani