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Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation

Bradley Efron
Journal of the American Statistical Association
Vol. 78, No. 382 (Jun., 1983), pp. 316-331
DOI: 10.2307/2288636
Stable URL: http://www.jstor.org/stable/2288636
Page Count: 16
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Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
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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 unbiased estimate, using only the original data. Cross-validation turns out to be related closely to the bootstrap estimate of the error rate. This article has two purposes: to understand better the theoretical basis of the prediction problem, and to investigate some related estimators, which seem to offer considerably improved estimation in small samples.

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