Bayesian matrix completion for hypothesis testing
针对毒理学数据稀疏问题，提出贝叶斯分层框架，跨化学物质和检测终点借用信息，实现活性预测、不确定性量化及多重假设检验校正，并同时建模异方差误差和非参数均值函数。
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.