Bayesian Factor Analysis for Inference on Interactions.

ABSTRACT: This article is motivated by the problem of inference on interactions among chemical exposures impacting human health outcomes. Chemicals often co-occur in the environment or in synthetic mixtures and as a result exposure levels can be highly correlated. We propose a latent factor joint model, which includes shared factors in both the predictor and response components while assuming conditional independence. By including a quadratic regression in the latent variables in the response component, we induce flexible dimension reduction in characterizing main effects and interactions. We propose a Bayesian approach to inference under this Factor analysis for INteractions (FIN) framework. Through appropriate modifications of the factor modeling structure, FIN can accommodate higher order interac

SUBMITTER: Ferrari F 

PROVIDER: S-EPMC8654343 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature