Dr. Wenyu Jiang

Department of Mathematics and Statistics

Queen's University

Title: " Review and Improvements on Prediction Accuracy Estimation for Microarray Data "

We first provide a critical review on some existing methods for estimating
prediction error in classifying microarray data where the number of genes
greatly exceeds the number of specimen. Special attention is given to the
bootstrap-related methods such as the popular .632+ bootstrap. 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 method which predicts for each specimen in the sample using bootstrap
learning sets of size ln. We then propose an adjusted bootstrap method that fits
a learning curve to the repeated leave-one-out bootstrap estimates calculated
with different bootstrap learning set sizes. The adjusted bootstrap method is
robust across the situations we investigate and provides slightly conservative
estimate for the prediction error. With small samples, it noticeably reduces
the bias of the leave-one-out bootstrap and the variability of the leave-one-out
cross-validation in microarray applications.