Abstract
This article surveys bootstrap methods for producing good approximate confidence intervals. The goal is to improve by an order of magnitude upon the accuracy of the standard intervals $\\hat{\\theta} \\pm z^{(\\alpha)} \\hat{\\sigma}$, in a way that allows routine application even to very complicated problems. Both theory and examples are used to show how this is done. The first seven sections provide a heuristic overview of four bootstrap confidence interval procedures: $BC_a$, bootstrap-t , ABC and calibration. Sections 8 and 9 describe the theory behind these methods, and their close connection with the likelihood-based confidence interval theory developed by Barndorff-Nielsen, Cox and Reid and others.
Keywords
Confidence intervalRobust confidence intervalsStatisticsConfidence distributionCDF-based nonparametric confidence intervalMathematicsHeuristicCalibrationComputer scienceMathematical optimization
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 11
- Issue
- 3
- Citations
- 2196
- Access
- Closed
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Cite This
Thomas J. DiCiccio,
Bradley Efron
(1996).
Bootstrap confidence intervals.
Statistical Science
, 11
(3)
.
https://doi.org/10.1214/ss/1032280214
Identifiers
- DOI
- 10.1214/ss/1032280214