Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

ABSTRACT: With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture com

SUBMITTER: Fu H 

PROVIDER: S-EPMC11247181 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature