Aggregated Projection Method: A New Approach for Group Factor Model

• 成果类型: Article; Early Access
• 署名作者: Hu, Jiaqi; Li, Ting; Wang, Xueqin
• 署名单位: Chinese Academy of Sciences; University of Science & Technology of China, CAS; Hong Kong Polytechnic University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2491154
• 发表日期: 2025
• 关键词: NUMBER

摘要

Identifying the global factors among grouped data is crucial in the group factor model. In this article, we propose a novel objective function for the task by maximizing the average of correlations between the latent global factors and group factors, solved through the eigen-decomposition of the aggregated projection matrix. Our method is not only computationally efficient but also robust to strongly correlated local factors. We establish the consistency of the global/local factor number estimation and the consistency and asymptotic distributions of the estimated global/local factors and loadings. Simulation studies show that our method outperforms state-of-the-art methods in various scenarios. The proposed method is applied to analyze the growth rate of house prices in the United States, identifying one global factor that significantly influences national house prices and several local factors that demonstrate the effects of each state. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.