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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 beExpand
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An Introduction to the Bootstrap
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
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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 FunctionExpand
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Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
This is a review of bootstrap methods, concentrating on basic ideas and applications rather than theoretical considerations. Expand
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Empirical Bayes Analysis of a Microarray Experiment
Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raisingExpand
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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 nearlyExpand
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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 likelihoodExpand
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Improvements on Cross-Validation: The 632+ Bootstrap Method
Abstract A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? This is an important question both for comparing models and forExpand
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On testing the significance of sets of genes
This paper discusses the problem of identifying dieren tially expressed groups of genes from a microarray experiment. The groups of genes are externally dened, for example, sets of gene pathwaysExpand
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