2019/04/27 by Arkaprava Roy, Roy, Arkaprava, Jana Schaich Borg +3 · 1 citation
Agricultural and Biological Sciences · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Mental Health Research Topics #Methodology (stat.ME) #Sensory Analysis and Statistical Methods
paper · doi:10.48550/arxiv.1904.12103
openalex publication_date 2019/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Promising methods have been developed to analyze these types of data in static cases, but only a few approaches are available for dynamic settings. To address this gap, we consider novel models and inference methods for pairs of matrices in which the columns correspond to multivariate observations at different time points. In order to characterize common and individual features, we propose a Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) that includes uncertainty in time alignment through an unknown warping function. We provide theoretical support for the proposed model, showing identifiability and posterior concentration. The structure enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm. We show excellent performance in simulations, and illustrate the method through application to a social mimicry experiment.