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Monday, March 9 2020
12:00pm - 1:00pm
Groseclose 402
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ISyE Statistic Seminar - Ruey S. Tsay

Title:

Statistical Learning for Big Dependent Data

Abstract:

Most works in the machine learning and high-dimensional statistical inference assume independence. Most data, on the other hand, are either dynamically or spatially dependent. In this talk, I discuss the impact of dependence on statistical inference of high-dimensional data analysis, including LASSO regression and generalized linear models. The presentation is based on some recent joint papers, focusing on modeling big dependent data for which either the dimension or the sample size or both are large. We demonstrate the analyses using PM2.5 data collected at multiple monitoring stations and at various frequencies.

Bio:

Ruey S. Tsay is H.G.B. Alexander Professor of Econometrics & Statistics, Booth School
of Business, University of Chicago. He earned his PhD from the University of Wisconsin
- Madison and was with Carnegie Mellon University before joining Chicago in 1989. His
research interest includes nancial econometrics, analysis of high-dimensional dependent
data, forecasting, and time-series analysis. He served as co-editor of the Journal of Busi-
ness and Economic Statistics from 1995 to 1997, Journal of Forecasting from 2006-2013,
and Statistica Sinica from 2014-2017. Currently, he is a co-editor of the Probability and
Statistics Book Series of Wiley. Professor Tsay published widely in leading econometric and statistical journals with more than 115 referred articles. He is the author of Analysis of Financial Time Series (3rd ed., 2010, Wiley), An Introduction to Analysis of Financial Data with R (2013, Wiley), and Multivariate Time Series Analysis (2014, Wiley), and co-author of Nonlinear Time Series Analysis (with R. Chen, 2019, Wiley) and Statistical Learning for Big Dependent Data (with D. Pe~na, 2020, Wiley, forthcoming). He received many honors and awards, including an elected member of Academia Sinica, Taiwan, and a fellow of the American Statistical Association and the Institute of Mathematical Statistics. He also serves on advisory boards of several research institutes and has given invited lectures at the IMF (Head quarter, Washington, DC) and Central Banks of several countries.