专家简介：夏寅，复旦大学管理学院教授，博导，2013年博士毕业于宾夕法尼亚大学，2013-2016年在美国北卡大学教堂山分校任tenure track Assistant Prof。2016年入选中组部千人计划青年项目；2020年获得国家自科基金优秀青年基金资助。研究方向包括高维统计推断、大范围检验及应用等。在JASA, AOS, JRSSB, Biometrika等期刊上发表二十余篇论文。
报告摘要: Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This talk develops a new class of locally-adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems and is fully data-driven. It is shown that LAWS controls the FDR asymptotically under mild conditions on dependence. The finite sample performance is investigated using simulated data, which demonstrates that LAWS controls the FDR and outperforms existing methods in power. The efficiency gain is substantial in many settings. We further illustrate the merits of LAWS through applications to the analysis of 2D and 3D images.
腾讯会议号：351 927 621
时间：2022年4月29日上午10:00
