TITLE: Top-down Statistical Modeling for Service Systems


Queueing theory has produced an extensive set of limit theorems that provide insight into the behavior of service systems in various asymptotic regimes. One key insight from this literature has been largely ignored over the years. In particular, the theory also identifies the key time scales and statistical parameters of the traffic that affect performance in these regimes. This suggests that statistical analysis of such traffic should therefore focus on those time scales and on accurately measuring those statistical parameters. In many real-world settings, this means that one should focus statistical attention on measuring (for example) long time-scale autocorrelation structure, and that fine-scale structure (like marginals of inter-arrival times) can largely be ignored. We illustrate this “top-down” approach in the context of analysis of high-intensity arrivals associated with call center data sets. Our talk will also discuss a suite of statistical tools that we are building that leverage this top-down perspective. These ideas are being developed jointly with Harsha Honnappa, Xiaowei Zhang, and Zeyu Zheng.

BIO:  Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University, and also holds a courtesy appointment in the Department of Electrical Engineering. He received his Ph.D in Operations Research from Stanford University in 1982. He then joined the faculty of the University of Wisconsin at Madison, where he held a joint appointment between the Industrial Engineering Department and Mathematics Research Center, and courtesy appointments in Computer Science and Mathematics. In 1987, he returned to Stanford, where he joined the Department of Operations Research. From 1999 to 2005, he served as Deputy Chair of the Department of Management Science and Engineering, and was Director of Stanford's Institute for Computational and Mathematical Engineering from 2006 until 2010. He served as Chair of MS&E from 2011 through 2015. He is a Fellow of INFORMS and a Fellow of the Institute of Mathematical Statistics, and was an IMS Medallion Lecturer in 1995 and INFORMS Markov Lecturer in 2014. He was co-winner of the Outstanding Publication Awards from the INFORMS Simulation Society in 1993, 2008, and 2016, was a co-winner of the Best (Biannual) Publication Award from the INFORMS Applied Probability Society in 2009, and was the co-winner of the John von Neumann Theory Prize from INFORMS in 2010. In 2012, he was elected to the National Academy of Engineering. He was Founding Editor-in-Chief of Stochastic Systems and is currently Editor-in-Chief of Journal of Applied Probability and Advances in Applied Probability. His research interests lie in simulation, computational probability, queueing theory, statistical inference for stochastic processes, and stochastic modeling.