  Consider a Gaussian nonparametric regression problem having both an unknown
mean function and unknown variance function. This article presents a class of
difference-based kernel estimators for the variance function. Optimal
convergence rates that are uniform over broad functional classes and bandwidths
are fully characterized, and asymptotic normality is also established. We also
show that for suitable asymptotic formulations our estimators achieve the
minimax rate.
