B. Efron

Publications326
h-index100
Citations114,945
Highly Influential Citations6,437
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Least angle regression
TLDR
A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
An Introduction to the Bootstrap
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…
Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
TLDR
The bootstrap is 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.
Empirical Bayes Analysis of a Microarray Experiment
TLDR
A simple nonparametric empirical Bayes model is introduced, which is used to guide the efficient reduction of the data to a single summary statistic per gene, and also to make simultaneous inferences concerning which genes were affected by the radiation.
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…
An Introduction to the Bootstrap.
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,…
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…
Improvements on Cross-Validation: The 632+ Bootstrap Method
TLDR
It is shown that a particular bootstrap method, the .632+ rule, substantially outperforms cross-validation in a catalog of 24 simulation experiments and also considers estimating the variability of an error rate estimate.
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