Functional principal component analysis with informative observation times.

ABSTRACT: Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serve as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis, often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven by outcome-related processes. Rather than ignoring the informative observation time process, we explicitly model the observational times by a general counting process dependent on time-varying prognostic factors. Identification of the mean, covariance function and functional principal components ensues via inverse intensity wei

SUBMITTER: Sang P 

PROVIDER: S-EPMC11771518 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature