Unsupervised Classification During Time-Series Model Building.

ABSTRACT: Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importa

SUBMITTER: Gates KM 

PROVIDER: S-EPMC8549846 | biostudies-literature | 2017 Mar-Apr

REPOSITORIES: biostudies-literature