论文标题
假设检验的贝叶斯矩阵完成
Bayesian Matrix Completion for Hypothesis Testing
论文作者
论文摘要
我们的目的是通过测定端点组合来推断每种化学物质的生物活性，以解决毒理学数据的稀疏性。我们提出了一个贝叶斯分层框架，该框架借用了不同化学品和测定终点的信息，促进了尚未分析的化学物质活动的样本外预测，量化了预测活性的不确定性，并调整了假设检验的多重性。此外，本文在毒理学方面进行了一种新颖的尝试，以同时对异性误差和非参数均值功能进行建模，从而导致对活动的更广泛的定义，其毒理学家提出了需求。实际应用确定了神经发育障碍和肥胖症最有效的化学物质。
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.
