The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 2, Number 2 (2008), 643-664.
A study of pre-validation
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.
Ann. Appl. Stat., Volume 2, Number 2 (2008), 643-664.
First available in Project Euclid: 3 July 2008
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Höfling, Holger; Tibshirani, Robert. A study of pre-validation. Ann. Appl. Stat. 2 (2008), no. 2, 643--664. doi:10.1214/07-AOAS152. https://projecteuclid.org/euclid.aoas/1215118532
- Supplementary material: Supporting online material for “A study of pre-validation”.