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

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 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Statistical Association Taylor & Francis

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

Journal of the American Statistical Association , Volume 78 (382): 16 – Jun 1, 1983

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

Journal of the American Statistical Association , Volume 78 (382): 16 – Jun 1, 1983

Abstract

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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References (12)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1537-274X
eISSN
0162-1459
DOI
10.1080/01621459.1983.10477973
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of the American Statistical AssociationTaylor & Francis

Published: Jun 1, 1983

Keywords: Bootstrap; Prediction problem; ANOVA decomposition; Logistic regression

There are no references for this article.