Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior

• 成果类型: Article
• 署名作者: Zhang, Ruqian; Zhang, Yijiao; Shen, Juan; Zhu, Zhongyi; Qu, Annie
• 署名单位: Fudan University; University of Pennsylvania; Pennsylvania Medicine; University of California System; University of California Santa Barbara
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2591232
• 发表日期: 2026-04-03
• 页码: 1167-1179
• 关键词: Data heterogeneity; High-dimensional Data; Similarity selection; sparsity; Variational Bayes; bayesian variable selection; variational inference; regression; gibbs

摘要

The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this article, we propose a novel Bayesian transfer learning method named CONCERT to allow robust partial information transfer for high-dimensional data analysis. A conditional spike-and-slab prior is introduced in the joint distribution of target and source parameters for information transfer. By incorporating covariate-specific priors, we can characterize partial similarities and integrate source information collaboratively to improve the performance on the target. In contrast to existing work, the CONCERT is a one-step procedure which achieves variable selection and information transfer simultaneously. We establish variable selection consistency, as well as estimation and prediction error bounds for CONCERT. Our theory demonstrates the covariate-specific benefit of transfer learning. To ensure the scalability of the algorithm, we adopt the variational Bayes framework to facilitate implementation. Extensive experiments and two real data applications showcase the validity and advantages of CONCERT over existing cutting-edge transfer learning methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.