- Productions scientifiques
The use in Biology of current high-throughput genetic and post-genomic data leads to the simultaneous evaluation of a huge number of statistical hypothesis and at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER), the False Discovery Rate (FDR) has appeared for the last ten years as the more appropriate criterion to handle the problem. One drawback is that the FDR is associated to a given rejection region for the statistic considered, without distinguishing those that are close to the boundary and those that are not.
As a result, the local FDR has been recently proposed to quantifies the specific probability, given the p-value, for being under the null hypothesis. In this context we present a semi-parametric approach based on kernel estimators.
This work is the result of the SSB group.