Related Experiment Video
Updated: Jul 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in
1Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, 6130 Executive Boulevard, Rockville, MD 20852, USA. wjiang@mathstat.concordia.ca
Abstract:
This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+ bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications.
Related Concept Videos
Bootstrapping
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
Quantifying and Rejecting Outliers: The Grubbs Test
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Comparing the Survival Analysis of Two or More Groups
Survival Tree
Building a Survival Tree
Constructing a survival tree begins...
