Factor-Adjusted Model Averaging

• 成果类型: Article
• 署名作者: Li, Wenhui; Zhang, Xinyu
• 署名单位: Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2576188
• 发表日期: 2026-04-03
• 页码: 1180-1191
• 关键词: Factor analysis; Frequentist model averaging; High-dimensional Regression; Jackknife model averaging; prediction; FACTOR-AUGMENTED REGRESSION; variable selection; number; RISK

摘要

We propose a model averaging method for high-dimensional regression with highly correlated covariates. We use a factor structure to model the covariate dependence, allowing the covariates to be decomposed into two uncorrelated or weakly correlated latent components: common factors and idiosyncratic components. The number of common factors is allowed to diverge. We average estimators from factor-adjusted candidate models with augmented predictors composed of estimated common factors and idiosyncratic components. We prove the asymptotic optimality in the sense of achieving the lowest squared loss and the consistency when correctly specified models exist in the model space. Numerical experiments and a real-data analysis illustrate the promising performance of the proposed method. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.