Program

Zoom Webinar Link

Schedule

All times are in the Central Time Zone.

• 11:00 – 11:10: Welcoming Remarks (Chair: Allison Theobold, Cal Poly)
  • Tim Hesterberg, ASA Section on Statistical Computing, Chair Elect
  • Ron Wasserstein, Executive Director, American Statistical Association

• 11:10 – 11:50: Keynote Address (Chair: Allison Theobold)
  • Speaker: Adrian Raftery,
    • Born in Ireland, Adrian E. Raftery is Blumstein-Jordan Professor Emeritus of Statistics and Sociology at the University of Washington. He develops new statistical methods for problems in the social, environmental and health sciences. An elected member of the U.S. National Academy of Sciences, he was identified as the world’s most cited researcher in mathematics for the decade 1995-2005 by Thomson-ISI. He has supervised 34 Ph.D. graduates, of whom 21 hold or have held tenure-track university faculty positions Extended Bio.
  • Title: Statistical Inference with Model Uncertainty
  • Abstract: Choosing a statistical model and accounting for uncertainty about this choice are important parts of the scientific process and are required for common statistical tasks such as parameter estimation, interval estimation, statistical inference, point prediction, and interval prediction. A canonical example is the choice of variables in a linear regression model. Many ways of doing this have been proposed, including Bayesian and penalized regression methods, and it is not clear which are best. We compare 21 popular methods via an extensive simulation study based on a wide range of real datasets. We found that three adaptive Bayesian model averaging methods performed best across all the statistical tasks and that two of these were also among the most computationally efficient. We also compared different priors on model space. Finally we addressed the question of whether model averaging provides an advantage over model selection. This is joint work with Anupreet Porwal.

• 11:50 – 13:05: Data Jamboree (Chair: Jackson Lautier, Bentley University)
  • Each language, in alphabetical order, will be allocated 12-minutes to tackle the same problems from webscraping data on Olympic athletes, followed by an analysis on athletes’ birth month.
    • Introduction to Webscraping
      • Kelly Bodwin will walk everyone through Webscraping 101, the nuts and bolts of how webscraping works.
    • Python: Susan VanderPlas,
      • Associate Professor, Department of Statistics, University of Nebraska-Lincoln.
      • Code
    • Julia: J. Brandon Carter
      • PhD Candidate, Department of Statistics and Data Science, University of Texas at Austin
      • Code
    • R: Kelly Bodwin
      • Associate Professor, Department of Statistics, Cal Poly
      • Code

• 13:10 - 14:15: Panel Discussion (Moderator: Steven Chiou)
  • Theme: AI in the Workplace
  • Panelists:
    • Jane Tan
      • Assistant Professor of Information Technology and Operations Management, SMU Cox School of Business
    • Noah Giansiracusa
      • Associate Professor of Mathematics at Bentley University
      • Author of How Algorithms Create and Prevent Fake News
    • Gail Burrill
      • Mathematics Specialist in PRIME (Program in Mathematics Education) at Michigan State University
      • Co-director of the Institute for Advanced Study’s International Seminar in the Park City Mathematics Institute