主 讲 人：王鹏飞
时间：2021年5月16日 上午9：00
地点：1706
报告摘要：The problems of large-scale two-sample inference often arise from the statistical analysis of "high throughput" data. The conventional multiple testing procedures for large-scale two-sample inference usually suffer from substantial loss of testing efficiency when conducting numerous two-sample t-tests directly. To some extent, this is due to the ignorance of sparsity information in large-scale two-sample inference. Moreover, in practice, the two-sample tests commonly have local correlations and neglecting the dependence structure in the two-sample tests may decrease the statistical accuracy in multiple testing. Therefore, it is imperative to develop a multiple testing procedure which not only takes into account the sparsity information but also accommodates the dependence structure among the tests. To address the aforementioned important issues, we start by introducing a novel dependence model to allow for sparsity information and to characterize the dependence structure among the tests. Based on the dependence model, we propose a Covariate Assisted Local Index of Significance (COALIS) procedure and show that it is valid and optimal in some sense. Then a data-driven procedure is developed to mimic the oracle procedure and simulations show that the COALIS procedure outperforms its competitors. Finally, we apply the COALIS procedure to the dosage response data.
报告人简介：王鹏飞博士，毕业于东北师范大学机器学习与生物信息学专业，师从朱文圣教授，为东北财经大学统计学院讲师。研究方向：大规模多重检验、生物统计、精准医疗，在BMC Bioinformatics，Genetics Research，TEST等国际期刊上发表多篇学术论文。
