© 1988 by Biometrika Trust
On bootstrap resampling and iteration
Department of Statistics, Australian National University Canberra, A.C.T. 2601, Australia
We propose a single unifying approach to bootstrap resampling, applicable to a very wide range of statistical problems. It enables attention to be focused sharply on one or more characteristics which are of major importance in any particular problem, such as coverage error or length for confidence intervals, or bias for point estimation. Our approach leads easily and directly to a very general form of bootstrap iteration, unifying and generalizing present disparate accounts of this subject. It also provides simple solutions to relatively complex problems, such as a suggestion by Lehmann (1986) for conditionally short confidence intervals.
Key Words: Bias reduction Bootstrap Confidence interval Coverage error Iterated bootstrap Jackknife Resampling Shrinkage
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