The R package FAMT (factor analysis for multiple testing) provides a powerful method for large-scale significance testing under dependence. It is especially designed to select differentially expressed genes in microarray data when the correlation structure among gene expressions is strong....

In multiple testing, a challenging issue is to provide an accurate estimation of the proportion [pi]0 of true null hypotheses among the whole set of tests. Besides a biological interpretation, this parameter is involved in the control of error rates such as the False Discovery Rate. Improving...

Item nonresponse is often dealt with through imputation. Marginal imputation, which consists of treating separately each variable requiring imputation, generally leads to biased estimators of parameters (e.g., coefficients of correlation) measuring relationships between variables. Shao and Wang...

A special type of modelling of interaction is investigated in the framework of two-way analysis of variance models for homologous factors. Factors are said to be homologous when their levels are in a meaningful one-to-one relationship, which arise in a wide variety of contexts, as recalled by...

Maximum likelihood estimation is investigated in the context of linear regression models under partial independence restrictions. These restrictions aim to assume a kind of completeness of a set of predictors "Z" in the sense that they are sufficient to explain the dependencies between an...