Structural breaks and shape constraints
- Qiyang Han, Rutgers University
- Axel Munk, University of Goettingen
- Richard Samworth, University of Cambridge
- Yi Yu, University of Warwick
Structural break analysis is concerned with the detection and localization of abrupt changes in the data generating distribution in time series and spatial processes. Shape-constrained inference, on the other hand, focuses on automatic learning that adapts to unknown structures of signals. Both topics are well-established in statistics, but the recent explosion of data has resulted in challenges in both fields to find theoretically guaranteed and computationally efficient statistical tools to harness and exploit such structural patterns. These challenges are ubiquitous in many, diverse application areas, such as security monitoring, neuroimaging, financial trading, ecological statistics, climate change, medical condition monitoring, sensor networks, risk assessment for disease outbreaks, flu trend analysis, genetics, electro-physiology and many others.
In the last few years, we have witnessed a growing body of literature in both communities focusing on similar problems, but we are also aware that communication between the two areas could be improved. This workshop focuses on recent developments in structural break analysis and shape-constrained inference, aiming to create a platform to bring the two communities together.