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Academic Lecture: Interval Data: Modeling and Visualization

Topic: Interval Data: Modeling and Visualization

Speaker: Dr. Dennis K. J. Lin,Purdue University 

Moderator:  Prof. LIN Huazhen, School of Statistics, SWUFE

Time: 10:30 am, Wednesday, November 3, 2021

Virtual Platform :Tencent Meeting ID: 670 666 992

Organizers: Center of Statistical Studies, School of Statistics, Office of International Exchange and Cooperation, Research Office

Speaker’s Profile

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).

Lecture Preview

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.