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Math @ Duke
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Publications [#375282] of David B. Dunson
search arxiv.org.Papers Published
- Jin, B; Dunson, DB; Rager, JE; Reif, DM; Engel, SM; Herring, AH, Bayesian matrix completion for hypothesis testing.,
Journal of the Royal Statistical Society. Series C, Applied statistics, vol. 72 no. 2
(May, 2023),
pp. 254-270 [doi]
(last updated on 2026/01/15)
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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