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复旦大学夏寅教授:Locally Adaptive Transfer Learning Algorithms for Large-Scale Multiple Testing

主 题Locally Adaptive Transfer Learning Algorithms for Large-Scale Multiple Testing

主讲人复旦大学夏寅教授

主持人统计学院周岭副教授

时间:2022年12月28日(周三)上午10:00-11:00

直播平台及会议ID:腾讯会议,ID: 472-858-013

主办单位:统计研究中心和统计学院 科研处

主讲人简介:

夏寅,复旦大学管理学院统计与数据科学系教授,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。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。

内容提要:

Transfer learning has enjoyed increasing popularity in a range of big data applications. In the context of large-scale multiple testing, the goal is to extract and transfer knowledge learned from related source domains to improve the accuracy of simultaneously testing of a large number of hypotheses in the target domain. This talk develops a locally adaptive transfer learning algorithm (LATLA) for transfer learning for multiple testing. In contrast with existing covariate-assisted multiple testing methods that require the auxiliary covariates to be collected alongside the primary data on the same testing units, LATLA provides a principled and generic transfer learning framework that is capable of incorporating multiple samples of auxiliary data from related source domains, possibly in different dimensions/structures and from diverse populations. Both the theoretical and numerical results show that LATLA controls the false discovery rate and outperforms existing methods in power. LATLA is illustrated through an application to genome-wide association studies for the identification of disease-associated SNPs by cross-utilizing the auxiliary data from a related linkage analysis.


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