Central Posterior Envelopes for Bayesian Longitudinal Functional Principal Component Analysis.

ABSTRACT: Longitudinally observed functional data are commonly encountered in biomedical studies. Under the weak separability assumption of the high dimensional covariance, the recently proposed Bayesian longitudinal functional principal component analysis (B-LFPCA) achieves the decomposition of the multidimensional signal into highly interpretable lower dimensional summaries, including eigenfunctions that capture directions of variation in the data along the longitudinal and functional dimensions. B-LFPCA provides uncertainty quantification of the estimated functional decomposition components through simultaneous parametric credible bands formed using the posterior sample. However, these traditional summaries are inherently based on point-wise summaries of the estimated functional components and do

SUBMITTER: Boland J 

PROVIDER: S-EPMC12716410 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature