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Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
Open accessAbstract
This is a review of bootstrap methods, concentrating on basic ideas and applications rather than theoretical considerations. It begins with an exposition of the bootstrap estimate of standard error for one-sample situations. Several examples, some involving quite complicated statistical procedures, are given. The bootstrap is then extended to other measures of statistical accuracy such as bias and prediction error, and to complicated data structures such as time series, censored data, and regression models. Several more examples are presented illustrating these ideas. The last third of the paper deals mainly with bootstrap confidence intervals.
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Metadata
- Authors
- Efron, B., & Tibshirani, R.
- ORCID
- Tibshirani, R.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1986-02-01
- Volume
- 1
- Issue
- 1
- Pages
- 54-75
- ISSN
- 0883-4237 (ISSN-L)
- 2168-8745
- OpenAlex
- W2112688502
- Cited by
- 6,033
- References
- 34
- Citations by year
- 2026: 83 · 2025: 148 · 2024: 208 · 2023: 220 · 2022: 254 · 2021: 330 · 2020: 306 · 2019: 331 · 2018: 289 · 2017: 244 · 2016: 253 · 2015: 234 · 2014: 245 · 2013: 232 · 2012: 228
- FWCI
- 41.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Science (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- bootstrapping, standard error estimation, confidence intervals, bias estimation, prediction error, time series, censored data, regression models, statistical accuracy
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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