2021/07/05 by Jin Liu, Liu, Jin
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2107.01773
openalex publication_date 2021/07/05 · openalex created_date 2021/07/19 · openalex updated_date 2026/07/28
Longitudinal processes often pose nonlinear change patterns. Latent basis growth models (LBGMs) provide a versatile solution without requiring specific functional forms. Building on the LBGM specification for unequally-spaced waves and individual occasions proposed by Liu and Perera (2023), we extend LBGMs to multivariate longitudinal outcomes. This provides a unified approach to nonlinear, interconnected trajectories. Simulation studies demonstrate that the proposed model can provide unbiased and accurate estimates with target coverage probabilities for the parameters of interest. Real-world analyses of reading and mathematics scores demonstrates its effectiveness in analyzing joint developmental processes that vary in temporal patterns. Computational code is included.