Central Posterior Envelopes for Bayesian Functional Principal Component Analysis.

ABSTRACT: Bayesian methods provide direct inference in functional data analysis applications without reliance on bootstrap techniques. A major tool in functional data applications is the functional principal component analysis which decomposes the data around a common mean function and identifies leading directions of variation. Bayesian functional principal components analysis (BFPCA) provides uncertainty quantification on the estimated functional model components via the posterior samples obtained. We propose central posterior envelopes (CPEs) for BFPCA based on functional depth as a descriptive visualization tool to summarize variation in the posterior samples of the estimated functional model components, contributing to uncertainty quantification in BFPCA. The proposed BFPCA relies on a latent f

SUBMITTER: Boland J 

PROVIDER: S-EPMC11178334 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature