Flexible Bayesian modeling for longitudinal binary and ordinal responses

• Published in: Statistics and computing Vol. 34; no. 6
• Main Authors: Kang, Jizhou, Kottas, Athanasios
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Science; Correlation analysis; Covariance; Data analysis; Gaussian process; Inference; Longitudinal studies; Modelling; Original Paper; Probability and Statistics in Computer Science; Statistical Theory and Methods; Statistics and Computing/Statistics Programs; Stochastic models; Stochastic processes; Synthetic data; Bayesian hierarchical modeling; Student-t process; Markov chain Monte Carlo; Functional data analysis; Continuation-ratio logits

Longitudinal studies with binary or ordinal responses are widely encountered in various disciplines, where the primary focus is on the temporal evolution of the probability of each response category. Traditional approaches build from the generalized mixed effects modeling framework. Even amplified w...