Individualized inference through fusion learning
Individualized inference through fusion learning
复制标题
通过融合学习进行个性化推理
DOI：
10.1002/wics.1498
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Xie, Min‐ge
中科院分区：
文献类型：
--
作者：
Cai, Chencheng;Chen, Rong;Xie, Min‐ge
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 thetargetindividual. This article provides a review on two individualized inference methods through fusion learning,iFusion andiGroup, 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 LearningStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Nonparametric MethodsData: Types and Structure > Massive Data
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DOI：
10.1080/01621459.2021.1947306
发表时间：
2019-06
影响因子：
3.7
作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
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发表时间：
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期刊：
影响因子：
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作者：
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敏感而坚定的$p$-价值观
DOI：
--
发表时间：
1991
期刊：
影响因子：
--
作者：
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通讯作者：
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