Importance sampling and the nested bootstrap

Abstract

The nested bootstrap is a computer-intensive technique for reducing the statistical error in the ordinary bootstrap. We show how the Monte Carlo technique of importance sampling can be used to substantially reduce the amount of computation needed in a simple double bootstrap confidence limit method.