Abstract
Advanced data acquisition technology has greatly increased the accessibility of complex inferences, based on summary statistics or sample data, from diverse data sources. Fusion learning refers to combining complex inferences from multiple sources to make a more effective overall inference. The tasks to focus on include: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently? 3) How to combine inferences to enhance an individual study, thus named i-Fusion? We present a general framework for nonparametric and efficient fusion learning. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD), developed by combining data depth, bootstrap and confidence distributions. We show that a depth-CD is an omnibus form of confidence regions, with the contours of level sets shrink toward the true parameter value, and thus an all-encompassing inferential tool. The approach is efficient, general and robust, and applicable to heterogeneous studies in broad ranges of complex settings. The approach is demonstrated with an aviation safety analysis in tracking aircraft landing performance and zero-event studies in clinical trials with non-estimable parameters
Organisation
Maria Pia Victoria Feser