B0673
Title: Multiple hypothesis screening via mixtures of non-local distributions with applications to genomic datasets
Authors: Francesco Denti - University of Padua (Italy) [presenting]
Abstract: The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of non-local densities to specify alternative distributions that enforce separation from the null and thus refine the screening procedure. Parametric and nonparametric model specifications are proposed. With a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we show the results of differential expression analyses conducted on publicly-available datasets from genomic studies of heterogeneous nature.
