E-values provide a principled foundation for false discovery rate control in high-dimensional microbiome data analysis. We show how their aggregation properties enable a derandomized, robust knockoff filter that outperforms classical approaches in stability and reproducibility.
Monti, G., Filzmoser, P. (2026). E-Values for Stable and Robust Variable Selection in Microbiome Studies. ERCIM NEWS, 145(July 2026), 18-19.
E-Values for Stable and Robust Variable Selection in Microbiome Studies
Monti, GS;
2026
Abstract
E-values provide a principled foundation for false discovery rate control in high-dimensional microbiome data analysis. We show how their aggregation properties enable a derandomized, robust knockoff filter that outperforms classical approaches in stability and reproducibility.
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