统计学系系列讲座之318-319期

统计学系系列讲座之318期

时间：2018年9月10日（周一）13:30-14:30

地点：史带楼502室

主持人：郁文 副教授 复旦大学管理学院统计学系

主题：Ghost Data

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：As natural as the real data, ghost data is everywhere—it is just data that you cannot see. We need to learn how to handle it, how to model with it, and how to put it to work. Some examples of ghost data are (a)    Virtual data—it isn’t there until you look at it; (b) Missing data—there is a slot to hold a value, but the slot is empty; (c) Pretend data—data that is made up; (d) Highly Sparse Data—whose absence implies a near zero, and (e) Simulation data—data to answer “what if.” For example, absence of evidence/data is not evidence of absence. In fact, it can be evidence of something. More Ghost Data can be extended to other existing areas: Hidden Markov Chain, Two-stage Least Square Estimate, Optimization via Simulation, Partition Model, Topological Data, just to name a few. Three movies will be discussed in this talk: (1) “The Sixth Sense” (Bruce Wallis)—I can see things that you cannot see; (2) “Sherlock Holmes” (Robert Downey)—absence of expected facts; and (3) “Edge of Tomorrow” (Tom Cruise)—how to speed up your learning (AlphaGo-Zero will also be discussed). It will be helpful, if you watch these movies before coming to my talk. This is an early stage of my research in this area--any feedback from you is deeply appreciated. Much of the basic idea is highly influenced via Mr. John Sall (JMP-SAS).

统计学系系列讲座之319期

时间：2018年9月4日（周二）15:00-16:00

地点：史带楼501室

主持人：郁文 副教授 复旦大学管理学院统计学系

主题：Stein's Method and Normal Approximations

主讲人：应志良  教授 Department of Statistics, Columbia University

摘要：The central limit theorem (normal approximation) has been a central tool in statistics. The classical Lindeberg-Feller version applies to a sum of independent random variables with a minimal condition. In many modern applications, such a version is not applicable. In this talk I will give a brief introduction to a useful alternative via Stein's method. I will show via examples the usefulness of this alternative approach.

                                                           统计学系 

2018-8-31