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Title: Bayesian semiparametric longitudinal functional mixed models with locally informative predictors Authors:  Abhra Sarkar - The University of Texas at Austin (United States) [presenting]
Abstract: A flexible Bayesian semiparametric mixed model is presented for longitudinal functional data analysis in the presence of potentially high-dimensional categorical covariates. The proposed method allows the fixed effects components to vary between dependent random partitions of the covariate space at different time points. The mechanism not only allows different sets of covariates to be included in the model at different time points but also allows the selected predictors' influences to vary flexibly over time. Smooth time-varying additive random effects are used to capture subject-specific heterogeneity. We establish posterior convergence guarantees for both function estimation and variable selection. We design a Markov chain Monte Carlo algorithm for posterior computation. We evaluate the methods empirical performances through synthetic experiments and demonstrate its practical utility through real-world applications.