Adaptive Partition Factor Analysis

• 成果类型: Article; Early Access
• 署名作者: Bortolato, Elena; Canale, Antonio
• 署名单位: Pompeu Fabra University; Barcelona School of Economics; University of Padua
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2621517
• 发表日期: 2026-04-21
• 关键词: Bayesian inference; factor models; Neural Networks; shrinkage priors; sparsity

摘要

Factor Analysis has traditionally been used across diverse disciplines to extrapolate latent traits that influence the behavior of multivariate observed variables. Historically, the focus has been on analyzing data from a single study, neglecting the potential study-specific variations present in data from multiple studies. Multi-study factor analysis has emerged as a recent methodological advancement that addresses this gap by distinguishing between latent traits shared across studies and study-specific components arising from artifactual or population-specific sources of variation. In this article, we extend the current Bayesian methodologies by introducing novel shrinkage priors for the latent factors, thereby accommodating a broader spectrum of scenarios-from the absence of study-specific latent factors to models in which factors pertain only to small subgroups nested within or shared between the studies. For the proposed construction we provide conditions for identifiability of factor loadings and guidelines to perform straightforward posterior computation via Gibbs sampling. Through comprehensive simulation studies, we demonstrate that our proposed method exhibits competing performance across a variety of scenarios compared to existing methods, yet providing richer insights. The practical benefits of our approach are further illustrated through applications to bird species co-occurrence data and ovarian cancer gene expression data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.