光华讲坛——海外名家讲堂第 20 期
主题： Interval Data: Modeling and Visualization
举办地点：腾讯会议号 670 666 992
主办单位： 统计研究中心和统计学院 国际交流与合作处 科研处
Dr. Dennis K. J. Lin is a Distinguished Professor and Head of statistics Department at Purdue University. His research interests are quality assurance, industrial statistics, data science, and response surface. He has published more than 250 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 and RSS, and a lifetime member of ICSA. He is an honorary chair professor for various universities, including Renmin University of China and Fudan University. 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), the Shewhart Medal (ASQ, 2015), the SPES Award (ASA-Spes, 2016), and the Deming Lecturer (JSM, 2020).
林共进，普渡大学杰出讲座教授兼统计系系主任。 他的研究兴趣是质量保证、工业统计、数据科学和响应面。他已公开发表250多篇SCI/SSCI论文。他目前或曾在十多家专业期刊担任AE，在Applied Stochastic Models for Business and Industry担任co-editor。他是ASA、IMS和ASQ的elected fellow，ISI和RSS的elected member，也是ICSA的终身成员。他也在包括中国人民大学和复旦大学担任首席教授。他的获奖项包括：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), the Shewhart Medal (ASQ, 2015), the SPES Award (ASA-Spes, 2016), and the Deming Lecturer (JSM, 2020).
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.
区间值数据是一种由区间的上下界组成的特殊符号数据。它可以产生于气候变化、股票价格波动、每日血压、大数据的聚集等多种情景。这种类型的数据包含对决策有用的丰富信息。因为区间的预测下限不应超过该区间的上限，区间值数据的预测是一项具有挑战性的任务。该项目提出了一种正则化人工神经网络 (RANN)模型来解决这个难题。该模型提供了预测精度和区间交叉之间的灵活权衡。实证研究表明了所提出方法的有效性和准确性。该项目的第二部分提出的segment plot 和dandelion plot弥补现有的区间数据可视化方法，并提供了更多信息。本部分还建立了用于解读这些新图的定理并举例说明。