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June 2008 A study of pre-validation
Holger Höfling, Robert Tibshirani
Ann. Appl. Stat. 2(2): 643-664 (June 2008). DOI: 10.1214/07-AOAS152

Abstract

Given a predictor of outcome derived from a high-dimensional dataset, pre-validation is a useful technique for comparing it to competing predictors on the same dataset. For microarray data, it allows one to compare a newly derived predictor for disease outcome to standard clinical predictors on the same dataset. We study pre-validation analytically to determine if the inferences drawn from it are valid. We show that while pre-validation generally works well, the straightforward “one degree of freedom” analytical test from pre-validation can be biased and we propose a permutation test to remedy this problem. In simulation studies, we show that the permutation test has the nominal level and achieves roughly the same power as the analytical test.

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Holger Höfling. Robert Tibshirani. "A study of pre-validation." Ann. Appl. Stat. 2 (2) 643 - 664, June 2008. https://doi.org/10.1214/07-AOAS152

Information

Published: June 2008
First available in Project Euclid: 3 July 2008

zbMATH: 1273.62126
MathSciNet: MR2524350
Digital Object Identifier: 10.1214/07-AOAS152

Rights: Copyright © 2008 Institute of Mathematical Statistics

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Vol.2 • No. 2 • June 2008
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