Bayesian Matrix Completion for Hypothesis Testing

2020/09/17 by Jin, Bora, Dunson, David B., Rager, Julia E. +3
#Applications (stat.AP) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2009.08405

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.

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