Seminar Details
Change-point Detection for COVID-19 Time Series via Self-normalization
- Start Date: April 7, 2021
- Event Start Time: 11:45 AM
- Event End Time: 12:45 PM
- Programs: DATA-INSPIRE TRIPODS Institute
- Seminar Series: DATA-INSPIRE TRIPODS Seminars
- Presenter(s): Xiaofeng Shao - University of Illinois, Urbana-Champaign
- Event Location: Online Event
- Event Additional Info: <p><strong>SPECIAL NOTE: This seminar is presented online only.</strong></p> <!--<p><strong>You can join via <a href="https://rutgers.webex.com/webappng/sites/rutgers/meeting/download/febd243c607f4fecbbedacf0284c9521?siteurl=rutgers&MTID=m483975b5197b0e7c911896b409cf8231">Webex</a></strong></p> <p>Meeting number (access code): 138 325 2462 </p> <p>Meeting password: 1234</p>-->
- Presentation Type: Stand Alone Presentation
- Abstract:
This talk consists of two parts. In the first part, I will review some basic idea of self-normalization (SN) for inference of time series in the context of confidence interval construction and change-point testing in mean. In the second part, I will present a piecewise linear quantile trend model to model infection trajectories of COVID-19 daily new cases. To estimate the change-points in the linear trend, we develop a new segmentation algorithm based on SN test statistics and local scanning. Data analysis for COVID-19 infection trends in many countries demonstrates the usefulness of our new model and segmentation method.
Bio:
Xiaofeng Shao is currently a professor at University of Illinois at Urbana-Champaign.
He is a fellow of Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). His research interests include: Time series analysis, functional data analysis, high dimensional data analysis and their applications in atmospheric science, business, economics, finance, and neuroscience.