2023 journal article

Bayesian matrix completion for hypothesis testing

Journal of the Royal Statistical Society Series C: Applied Statistics.

By: B. Jin*, D. Dunson*, J. Rager*, D. Reif, S. Engel* & A. Herring*

Source: ORCID
Added: January 4, 2023

Abstract We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.