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Title: Fusion learning and confederate inference Authors:  Peter Song - University of Michigan (United States) [presenting]
Abstract: Data analytics and statistical algorithms in data integration are considered. As data sets of related studies become more easily accessible, combining data sets of similar studies is undertaken in practice to achieve Big Data and to enjoy more powerful analysis. A major challenge arising from integrated data analytics pertains to principles of information aggregation, learning data heterogeneity, algorithms for model fusion. Information aggregation has been studied extensively by many statistics pioneers, which lay down the foundation of data integration. Also, ignoring such heterogeneity in data analysis may result in biased estimation and misleading inference. Distributed computing and inference will be discussed.