The Statistics Triangle 统计学的三角形
【时间】3月7日 周四 上午10:00
【地点】九龙湖 润良报告厅
【协办学院】数学学院
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【主讲人简介】
金加顺，卡耐基梅隆大学统计与数据科学系教授，早期的主要研究领域是大规模稀疏数据的数据分析推断，近期的研究兴趣主要集中在社会网络，获得了美国数理统计学会现任会士（IMS Fellow)、特威迪奖（IMS Tweedie Award）、 勋章讲座（IMS Medallion Lecture)、 应用统计年鉴特邀讲座（IMS AOAS Lecture）、 美国统计学会会士（ASA Fellow)、 顶级统计期刊主编特邀评述专辑论文（Editor’s Invited Review Paper）和主编特邀评论论文（Editor’s Discussion paper）等多项荣誉。并有着异常丰富的业界经历，包括近年华尔街全球最成功的量化对冲基金巨头（Two-Sigma Investment）数据科学团队全职工作两年的研发经验等。
【内容提要】
In his Fisher’s Lecture in 1996, Efron suggested that there is a philosophical triangle in statistics with “Bayesian”, “Fisherian”, and “Frequentist” being the three vertices, and most of the statistical methods can be viewed as a convex linear combination of the three philosophies. We collected and cleaned a data set consisting of the citation and bibtex (e.g., title, abstract, author information) data of 83,331 papers published in 36 journals in statistics and related fields, spanning 41 years. Using the data set, we constructed 21 co-citation networks, each for a time window between 1990 and 2015. We propose a dynamic Degree-Corrected Mixed- Membership (dynamic-DCMM) model, where we model the research interests of an author by a low-dimensional weight vector (called the network memberships) that evolves slowly over time. We propose dynamic-SCORE as a new approach to estimating the memberships. We discover a triangle in the spectral domain which we call the Statistical Triangle, and use it to visualize the research trajectories of individual authors. We interpret the three vertices of the triangle as the three primary research areas in statistics: “Bayes”, “Biostatistics” and “Nonparametrics”. The Statistical Triangle further splits into 15 sub-regions, which we interpret as the 15 representative sub-areas in statistics. These results provide useful insights over the research trend and behavior of statisticians.
【讲座论文事项】
讲座论文主题：The Statistics Triangle
论文命名“学号-姓名-讲座论文.pdf”，提交截止时间为讲座后第7日晚23:59。提交方式与注意事项详见附件。
【附件】
本科生课外研学讲座活动指南.pdf（研学讲座参与方式、讲座论文提交方式、研学学分认定标准等）
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