Biased-Bootstrap Recycling Brett Presnell University Of Florida  Bootstrap recycling exploits the importance sampling identity to significantly reduce the computational burden in the iterated bootstrap.  Unfortunately recycling is directly applicable only in parametric bootstrapping.  We show that this limitation can be overcome by embedding the empirical distribution in a nonparametric least favorable family, producing a pseudo-parametric double bootstrap that is amenable to recycling while retaining the desirable properties of the usual nonparametric double bootstrap.  As an illustration, we show that Efron's bootstrap percentile interval is second-order correct after calibration by this modified double bootstrap