统计学系系列讲座之356-358期

统计学系系列讲座之356

 

时 间:2019年10月14日(星期一)10:30-11:30

地 点:史带楼502室

主持人:张新生  教授 复旦大学管理学院统计学系

主 题:Transfer Learning for Nonparametric Classification

主讲人:Professor Tony Cai(蔡天文)

    The Wharton School, University of Pennsylvania

简 介:

蔡天文教授是沃顿商学院Dorothy Silberberg 统计学讲席教授。2008年荣获世界统计学考普斯奖 (COPSS Presidents' Award), 该奖为国际统计学领域的最高奖。2006年当选为国际数理统计学会会士(Fellow)。他曾担任The Annals of Statistics的主编,也曾担任Journal of the American Statistical Association,Annals of Statistics,Statistica Sinica等国际著名统计期刊的编委(Associate Editor),现任Journal of the Royal Statistical Society, Series B的编委(Associate Editor)。其主要研究方向为:高维统计推断 (High dimensional statistical inference)、大范围假设检验 (Large-scale multiple comparisons)、非参数函数估计 (Nonparametric function estimation)等。

摘 要:

Human learners appear to have a talent to transfer their knowledge gained from one task to another similar but different task. However, in statistical learning, most procedures are designed to solve one single task, or to learn one single distribution. In this talk, we consider transfer learning based on observations from different distributions under the posterior drift model, which is a general framework for many applications. 

We first establish the minimax rate of convergence and construct a rate-optimal two-sample weighted K-NN classifier. The results characterize precisely the contribution of the observations from the source distribution to the classification task under the target distribution. A data driven adaptive classifier is then introduced and is shown to simultaneously attain within a log factor of the optimal rate over a wide collection of parameter spaces.  Extensions to multiple source distributions will also be discussed.

 
统计学系系列讲座之357

 

时 间:2019年10月15日(星期二)16:00-17:00

地 点:史带楼302室

主持人:朱仲义  教授 复旦大学管理学院统计学系

主 题:Challenges and Opportunities(大数据时代的统计学: 机遇和挑战)

主讲人:Professor Xuming He(何旭铭)

    Chair and H. C. Carver Professor of Statistics,Department of Statistics University of Michigan

摘 要:

A recent draft report sponsored by the US National Science Foundation stated that Statistics is at a crossroads in the data science era. As part of the steering committee for the report, I have learned a lot from broad discussions over the past year. I will go over some of the main findings in the report, from research trends to education/training to cultural changes in the field of statistics. In particular, I will discuss emerging research topics and emerging applications that will require new statistical foundations, methodology, and computational thinking, as well as the need to develop more comprehensive evaluation metrics of scholarship in statistics. It is clear that the field of Statistics is no longer what it was thirty years ago, and it is important that it stays on course to play a leadership role in data science and data engineering. This presentation will not be technical in nature and aims to promote further discussions about the future of statistics in the local context. 

 

统计学系系列讲座之358

 

时 间:2019年10月18日(星期五)10:30-11:30

地 点:史带楼603室

主持人:郁文  教授 复旦大学管理学院统计学系

主 题:Interval Data: Modeling and Visualization

主讲人:Dennis Kon-Jin Lin,

    University Distinguished Professor

    Department of Statistics, The Pennsylvania State University, USA.

简 介:

Dr. Dennis K. J. Lin is a university distinguished professor of supply chain and statistics at Penn State University. His research interests are quality assurance, industrial statistics, data mining, and response surface. He has published near 200 SCI/SSCI papers in a wide variety of journals. He currently serves or has served as associate editor for more than 10 professional journals and was co-editor for Applied Stochastic Models for Business and Industry. Dr. Lin is an elected fellow of ASA, IMS and ASQ, an elected member of ISI, a lifetime member of ICSA, and a fellow of RSS. He is an honorary chair professor for various universities, including Renmin University of China (as a Chang-Jiang Scholar), Fudan University, and National Chengchi University (Taiwan). His recent awards including, the Youden Address (ASQ, 2010), the Shewell Award (ASQ, 2010), the Don Owen Award (ASA, 2011), the Loutit Address (SSC, 2011), the Hunter Award (ASQ, 2014), and the Shewhart Medal (ASQ, 2015). This yea, he is awarded SPES Award at the 2016 Joint Statistical Meeting.

摘 要:

Interval-valued data is a special symbolic data composed of lower and upper bounds of intervals. It can be generated from the change of climate, fluctuation of stock prices, daily blood pressures, aggregation of large datasets, and many other situations. Such type of data contains rich information useful for decision making. The prediction of interval-valued data is a challenging task as the predicted lower bounds of intervals should not cross over the corresponding upper bounds. In this project, a regularized artificial neural network (RANN) is proposed to address this difficult problem. It provides a flexible trade-off between prediction accuracy and interval crossing. Empirical study indicates the usefulness and accuracy of the proposed method. The second portion of this project provides some new insights for visualization of interval data.  Two plots are proposed—segment plot and dandelion plot.  The new approach compensates the existing visualization methods and provides much more information.  Theorems have been established for reading these new plots.  Examples are given for illustration.

 

 统计学系 

2019-10-10