Data Science Seminar
Hosted by Department of Mathematical Sciences
This talk is part of the Dean's Speaker Series in Statistics and Data Science
Inferences from different data sources can often be fused together to yield more powerful findings than those from individual sources alone. We present a new approach for fusion learning by using data depth and the so-called confidence distributions (CD). We further develop the individualized fusion learning, ‘iFusion’, for drawing efficient individualized inference by fusing the leanings from relevant data sources. This approach is robust for handling heterogeneity arising from diverse data sources, and is ideally suited for goal-directed applications such as precision medicine. The approach is demonstrated by simulation studies and applications in risk modeling of aircraft landing performance.