Expandable factor analysis

• 成果类型: Article
• 署名作者: Srivastava, Sanvesh; Engelhardt, Barbara E.; Dunson, David B.
• 署名单位: University of Iowa; Princeton University; Duke University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx030
• 发表日期: 2017
• 页码: 649663
• 关键词: nonconcave penalized likelihood; Principal Component Analysis; approximate factor models; variable selection; number

摘要

Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation of a low-rank and sparse loadings matrix. The structure of the prior leads to an estimation algorithm that accommodates uncertainty in the number of factors. We propose an information criterion to select the hyperparameters of the prior. Expandable factor analysis has better false discovery rates and true positive rates than its competitors across diverse simulation settings. We apply the proposed approach to a gene expression study of ageing in mice, demonstrating superior results relative to four competing methods.