Let (X, Y) be an d--valued regression pair, whereXhas a density andYis bounded. Ifni.i.d. samples are drawn from this distribution, the Nadaraya-Watson kernel regression estimate in dwith Hilbert kernelK(x)=1/||x||dis shown to converge weakly for all such regression pairs. We also show that...

In statistics it is necessary to study the relation among many probability distributions. Information geometry elucidates the geometric structure on the space of all distributions. When combined with Bayesian decision theory, it leads to the new concept of "ideal estimates." They uniquely exist...