  Fully robust OSCV is a modification of the OSCV method that produces
consistent bandwidth in the cases of smooth and nonsmooth regression functions.
The current implementation of the method uses the kernel $H_I$ that is almost
indistinguishable from the Gaussian kernel on the interval $[-4,4]$, but has
negative tails. The theoretical properties and practical performances of the
$H_I$- and $\phi$-based OSCV versions are compared. The kernel $H_I$ tends to
produce too low bandwidths in the smooth case. The $H_I$-based OSCV curves are
shown to have wiggles appearing in the neighborhood of zero. The kernel $H_I$
uncovers sensitivity of the OSCV method to a tiny modification of the kernel
used for the cross-validation purposes. The recently found robust bimodal
kernels tend to produce OSCV curves with multiple local minima. The problem of
finding a robust unimodal nonnegative kernel remains open.
