A0428
Title: Harnessing the collective wisdom: Fusion learning using decision sequences from diverse sources
Authors: Trambak Banerjee - University of Kansas (United States) [presenting]
Abstract: Learning from the collective wisdom of crowds enhances the transparency of scientific findings by incorporating diverse perspectives into the decision-making process. Synthesizing such collective wisdom is related to the statistical notion of fusion learning from multiple data sources or studies. However, fusing inferences from diverse sources is challenging since cross-source heterogeneity and potential data-sharing complicate statistical inference. An integrative ranking and thresholding (IRT) framework is proposed for fusion learning in multiple testing. IRT operates under the setting where, from each study, a triplet is available: the vector of binary accept-reject decisions on the tested hypotheses, the study-specific false discovery rate (FDR) level and the hypotheses tested by the study. Under this setting, IRT constructs an aggregated, nonparametric, and discriminatory measure of evidence against each null hypotheses, which facilitates ranking the hypotheses in the order of their likelihood of being rejected. IRT guarantees an overall FDR control under arbitrary dependence between the evidence measures as long as the studies control their respective FDR at the desired levels, and a comprehensive numerical study demonstrates that it is a powerful framework for pooling inferences.
