Cited by 6,033Statistical Methods and InferenceProbability and Statistical ResearchStatistical Methods and Bayesian Inference

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Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy

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Efron, B., Tibshirani, R.·1986·Statistical Science

DOI 10.1214/ss/1177013815

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Abstract

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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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
DOI: 10.1214/ss/1177013815
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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