A parametric version of jackknife-after-bootstrap

We investigate the problem of deriving precision estimates for bootstrap quantities within parametric families. B. Efron´s (1992) jackknife-after-bootstrap is a simple approach that only uses the information in the original bootstrap samples via the importance sampling technique, with no further resampling required. This method can be applied to many Monte Carlo experiments, especially, to the parametric input modeling problems. Variance analysis of the parametric jackknife-after-bootstrap is discussed. Under some reasonable conditions, the parametric jackknife-after-bootstrap method is as good as the true jackknife method. A generalized parametric jackknife-after-bootstrap method is introduced