A Latent Variable Approach to Learning High-Dimensional Multivariate Longitudinal Data

• 成果类型: Article; Early Access
• 署名作者: Lee, Sze Ming; Chen, Yunxiao; Sit, Tony
• 署名单位: University of London; London School Economics & Political Science; Chinese University of Hong Kong
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2606384
• 发表日期: 2026-03-07
• 关键词: Factor Model; Missing Data; recurrent event data; item response theory; factor models; regression; association; discrete; number

摘要

High-dimensional multivariate longitudinal data, which arise when many outcome variables are measured repeatedly over time, are becoming increasingly common in social, behavioral and health sciences. We propose a latent variable model for drawing statistical inferences on covariate effects and predicting future outcomes based on high-dimensional multivariate longitudinal data. This model introduces unobserved factors to account for the between-variable and across-time dependence and assist the prediction. Statistical inference and prediction tools are developed under a general setting that allows outcome variables to be of mixed types and possibly unobserved for certain time points, for example, due to right censoring. A central limit theorem is established for drawing statistical inferences on regression coefficients. Additionally, an information criterion is introduced to choose the number of factors. The proposed model is applied to customer grocery shopping records to predict and understand shopping behavior. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.