The bootstrap approximates the shape of the sampling distribution by simulating replicate experiments on the basis of the data we have observed.

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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.

Studentization is accomplished by dividing the location estimator by the sample analog of its asymptotic standard deviation. The importance-sampling results obtained for bootstrap replication sizes 10 ...