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- W3085319754 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." @default.
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- W3085319754 date "2020-09-17" @default.
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- W3085319754 title "Bayesian Matrix Completion for Hypothesis Testing" @default.
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- W3085319754 doi "https://doi.org/10.48550/arxiv.2009.08405" @default.
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