Individualized inference through fusion learning

Fusion learning methods, developed for the purpose of analyzing datasets from many different sources, have become a popular research topic in recent years. Individualized inference approaches through fusion learning extend fusion learning approaches to individualized inference problems over a heterogeneous population, where similar individuals are fused together to enhance the inference over the target individual. Both classical fusion learning and individualized inference approaches through fusion learning are established based on weighted aggregation of individual information, but the weight used in the latter is localized to the target individual. This article provides a review on two individualized inference methods through fusion learning, i Fusion and i Group, that are developed under different asymptotic settings. Both procedures guarantee optimal asymptotic theoretical performance and computational scalability. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Manifold Learning Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Data: Types and Structure > Massive Data