A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data.

ABSTRACT: The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to

SUBMITTER: Denti F 

PROVIDER: S-EPMC10120855 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature