Morning, December 03, 2025

Tutorial 1: Introduction to Nonlinear Time Series: Concepts and Applications (2 hours)

Lecturer: Prof. Rong Chen, Rutgers University

Dr. Chen is a Distinguished Professor of Statistics and Chair of the Department of Statistics at Rutgers University. His teaching and research focus on complex time series analysis, dynamic systems, Monte Carlo methods, and statistical applications in bioinformatics, business, economics, and engineering. He is an elected Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. Dr. Chen has held several editorial positions, including co-editor of the Journal of Business and Economic Statistics and Statistica Sinica. He currently serves as the president-elect of the International Chinese Statistical Association and has previously been Treasurer of the Institute of Mathematical Statistics.
Additionally, he was a program director in the Division of Mathematical Sciences at the National Science Foundation. Dr. Chen earned his Ph.D. and M.S. in Statistics from Carnegie Mellon University and his B.S. in Mathematics from Peking University in China.

Tutorial Description:
This intensive two-hour tutorial offers a focused introduction to nonlinear time series analysis. The session will cover foundational concepts and introduce some selected nonlinear time series models, including threshold autoregressive models, nonlinear regime-switching models, ARCH/GARCH models, and nonparametric approaches for nonlinear time series, with an emphasis on understanding when and why nonlinear models are appropriate. Through a combination of conceptual discussion and practical demonstrations using real-world data, model building, estimation and model checking procedures are introduced and illustrated.

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Afternoon, December 03, 2025

Tutorial 2: Introduction to Markov Chain Monte Carlo for Bayesian Computation (2 hours)

Lecturer: Prof. Fabrizio Ruggeri, ISI President

Fabrizio Ruggeri (B.Sc. Mathematics Milano, M.Sc. Statistics Carnegie Mellon, Ph.D. Statistics Duke) is Senior Fellow at the Istituto di Matematica Applicata e Tecnologie Informatiche (IMATI) in Milano of CNR (Consiglio Nazionale delle Ricerche) where he had been a researcher from 1988 to May 2023 (as Research Director since 2001). He was a member until 2024 of the Faculty of the Ph.D. programme in Mathematics at the universities of Milano-Bicocca and Pavia. He had various international  appointments, including Adjunct Professor at Queensland University of Technology (Brisbane, Australia), International Professor Affiliate at Polytechnic Institute (New York University, USA), Chair of Excellence at Universidad Carlos III and ICMAT-CSIC (Madrid, Spain) and Faculty of the Ph.D programme in Statistics at the Universidad de Valparaiso (Chile). He is the President-Elect of the International Statistical Institute (ISI) for the 2023-2025 term, after which he will serve as President from 2025 to 2027. He has been President of ENBIS (European Network for Business and Industrial Statistics), ISBA (International Society for Bayesian Analysis) and ISBIS (International Society for Business and Industrial Statistics), and ISI Vice President. He is a Fellow of IMS (Institute of Mathematical Statistics), ASA (American Statistical Association) and ISBA (which also awarded him the first Zellner Medal), and ENBIS Honorary Member. He is past Editor-in-Chief of Applied Stochastic Models in Business and Industry (2007-24) and currently of Wiley StatsRef, an online encyclopedia. He is the Director of the Applied Bayesian Statistics summer school organized by CNR-IMATI since 2004 and Chair of the series of workshops on Bayesian Inference in Stochastic Processes. He is a member of the Advisory Scientific Committee of the Institute of Statistics of Academia Sinica, Taiwan. He is author of over 200 articles (including 130 in refereed journals) and author/editor of 6 books. His interests are mostly in Bayesian Statistics and Decision Analysis, especially about robustness, stochastic processes and industrial applications, mainly in reliability. His interests also cover other areas, in particular concentration functions, wavelets, healthcare frauds and Adversarial Risk Analysis.

Tutorial Description:

The introductory course on Bayesian computation will present an example of conjugate analysis (gamma prior for an exponential model), another about Bayesian updating for discrete events and two examples about the use of Markov chain Monte Carlo (MCMC) methods in linear and logistic regression. Computations in closed form will be presented especially for the first example, while R routines will be widely used for the others.