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Large-Scale Covariate Assisted Two-Sample Inference under Dependence

发布时间:2019-11-01 作者: 浏览次数:
Speaker: 朱文圣 DateTime: 2019-11-1 下午3:00
Brief Introduction to Speaker:

朱文圣,东北师范大学,教授。

Place: 六号楼2楼报告厅
Abstract: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 can not only take into account the sparsity information but also accommodate the dependence structure among the tests. To address the aforementioned important issues, we first introduce a novel dependence model to allow for sparsity information and to characterize the dependence structure among the tes...