摘要：A nonparametric regression estimator is introduced which adapts to the smoothness
of the unknown function being estimated. This property allows the new estimator
to automatically achieve minimal bias over a large class of locally smooth functions
without changing the rate at which the variance converges. Optimal convergence rates
are shown to hold for both i.i.d. data and autoregressive processes satisfying strong
mixing conditions
