Adaptive asymptotically efficient estimation in heteroscedastic   nonparametric regression via model selection
  The paper deals with asymptotic properties of the adaptive procedure proposed in the author paper, 2007, for estimating a unknown nonparametric regression. We prove that this procedure is asymptotically efficient for a quadratic risk, i.e. the asymptotic quadratic risk for this procedure coincides with the Pinsker constant which gives a sharp lower bound for the quadratic risk over all possible estimators. 
Leonid Galtchouk (IRMA), Serguey Pergamenshchikov (LMRS)