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Fusion and Individualized Fusion Learning from Diverse Data Sources by Confidence Distribution

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STSW02 - Statistics of geometric features and new data types

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 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. In essence, iFusion strategically ‘borrows strength’ from relevant individuals to improve efficiency while retaining its inference validity. Computationally, the fusion approach here is parallel in nature and scales up well in comparison with competing approaches. The performance of the approach is demonstrated by simulation studies and risk valuation  of aircraft landing data.


This talk is part of the Isaac Newton Institute Seminar Series series.

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