It is a common practice to use resampling methods such as the bootstrap for calculating the p-value for each test when … performing large scale multiple testing. The precision of the bootstrap p-values and that of the false discovery rate (FDR … important need, we developed a simple adaptive bootstrap methodology for large scale multiple testing, which reduces the total …

resampling based methods, such as permutation methods, the null-centered and scaled bootstrap (NCSB) method, and the quantile-transformed-bootstrap … suffer from that limitation, but an impractical number of bootstrap samples are often needed to get proper control of Type I …

The bootstrap is a tool that allows for efficient evaluation of prediction performance of statistical techniques … evaluated has to be applied to every bootstrap sample in the same manner it would be used on new data. This includes a selection … simulation study that complexity selection in conventional bootstrap samples, drawn with replacement, is severely biased in many …

generally asymptotically valid null distribution for the test-statistics and a corresponding bootstrap estimate, whose marginal …

error rate. A general bootstrap algorithm is supplied to conveniently obtain consistent estimators of the null distribution. …

methods and compared their performance in terms of empirical coverage and width. We developed a bootstrap case cross …

error rate. A general bootstrap algorithm is supplied to conveniently obtain consistent estimators of the null distribution … among the rejected hypotheses (van der Laan et al., 2004a). The proposed bootstrap multiple testing procedures are evaluated …

error rate. A general bootstrap algorithm is supplied to conveniently obtain consistent estimators of the null distribution. …

generally asymptotically valid null distribution for the test-statistics and a corresponding bootstrap estimate, whose marginal …