Model-free global likelihood subsampling for massive data
报告专家：周永道（南开大学）
报告时间：2023年10月30日上午8：45-9：45
报告地点：西南中心516
报告摘要：Most existing studies for subsampling heavily depend on a specified model. If the assumed model is not correct, the performance of the subsample may be poor. Thistalkfocuses on a model-free subsampling method, called global likelihood subsampling, such that the subsample is robust to different model choices. It leverages the idea of the global likelihood sampler, which is an effective and robust sampling method from a given continuous distribution. Furthermore, we accelerate the algorithm for largescale datasets and extend it to deal with high-dimensional data with relatively low computational complexity. Simulations and real data studies are conducted to apply the proposed method to regression and classification problems. It illustrates that this method is robust against different modeling methods and has promising performance compared with some existing model-free subsampling methods for data compression.
专家简介：周永道，南开大学统计与数据科学学院教授、博导，入选国家高水平人才青年项目、天津市创新类领军人才、南开大学百名青年学科带头人。研究方向为试验设计和大数据分析。主持过五项国家自然科学基金、一项天津市自然科学基金重点项目及其它十余项纵横向项目。曾长短期访问加州大学洛杉矶分校等五所境外高校。在统计学和机器学习顶刊JRSSB、JASA、Biometrika、TKDE及中国科学等国内外重要期刊发表学术论文60多篇；合作出版了六部中英文专著和教材。曾获全国统计科学研究优秀成果奖一等奖及全国统计科学技术进步奖三等奖。现为天津市现场统计研究会理事长、中国现场统计研究会多元分析分会副理事长、中国数学会均匀设计分会秘书长。
邀请人：宋恩彬