B. Efron

Publications223
h-index68
Citations85,740
Highly Influential Citations4,567
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Least angle regression
The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be… Expand
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An Introduction to the Bootstrap
TLDR
Introduction The Accuracy of a Sample Mean Random Samples and Probabilities The Empirical Distribution Function and the Plug-In Principle Standard Errors and Estimated Standard Errors The Bootstrap Estimate of Standard Error Bootstrap Standard Errors: Some Examples More Complicated Data Structures Regression Models Estimates of Bias The Jackknife Confidence Intervals Based on Bootstrap "Tables" and Bootstrap Percentiles Efficient Bootstrap Computations Approximate Likelihoods Bootstrap Bioequivalence. Expand
  • 16,515
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The jackknife, the bootstrap, and other resampling plans
The Jackknife Estimate of Bias The Jackknife Estimate of Variance Bias of the Jackknife Variance Estimate The Bootstrap The Infinitesimal Jackknife The Delta Method and the Influence Function… Expand
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Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
TLDR
This is a review of bootstrap methods, concentrating on basic ideas and applications rather than theoretical considerations. Expand
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An Introduction to the Bootstrap.
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Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
Abstract We construct a prediction rule on the basis of some data, and then wish to estimate the error rate of this rule in classifying future observations. Cross-validation provides a nearly… Expand
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  • 164
Better Bootstrap Confidence Intervals
Abstract We consider the problem of setting approximate confidence intervals for a single parameter θ in a multiparameter family. The standard approximate intervals based on maximum likelihood… Expand
  • 2,714
  • 142
Bootstrap Methods: Another Look at the Jackknife
We discuss the following problem given a random sample X = (X 1, X 2,…, X n) from an unknown probability distribution F, estimate the sampling distribution of some prespecified random variable R(X,… Expand
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  • 110
  • PDF
An introduction to the bootstrap
Statistics is the science of learning from experience, especially experience that arrives a little bit at a time. The earliest information science was statistics, originating in about 1650. This… Expand
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Bootstrap confidence intervals
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 0 ? z(a), in a… Expand
  • 1,465
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