Rates of Expansions for Functional Estimators

In this paper, we summarize results on convergence rates of various kernel based non- and semiparametric estimators, focusing the impact insufficient distributional smoothness, possibly unknown smoothness even non-existence density. presence a possible lack uncertainty about methods safeguarding against are surveyed with emphasis nonconvex model averaging. This approach can be implemented via combined estimator that selects weights minimizing asymptotic mean squared error. order to evaluate finite sample performance these similar estimators argue it is important account for smoothness.